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Pipeline run

1ec67ae0-ca43-4c79-a511-ae7186c85096

Pipeline LLM cost (USD)
API 1: $0.0076 API 2: $0.0002 API 3: $0.0000 Total: $0.0078

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description
Nature of work · Machine Learning / Applied Science
Design and run ML experiments on large datasets, build Python/Deep Neural Network models in Azure ML/Databricks/Spark, and deploy them into production while using SQL and collaborating to ship results.
""experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.""
Tech stack maturity
Mainstream Modern
The skill set centers on widely adopted cloud and data platform technologies like Azure, Azure ML, Spark, Hive, Python, and SQL, which are characteristic of modern but established enterprise ML stacks.
AI index (0 = no AI use, 5 = totally AI-dependent · v2.1)
3.20 / 5
Title match
Has AI skill
AI skill (primary)
· AI skill (secondary)
· On AI team
· Builds AI products
vocab breakdown (legacy)
Assistants (×1):
Frameworks (×2):
Models / concepts (×3): ML, Machine Learning
Evidence — skills matched in JD (12)
Python Azure ML Deep Neural Network Azure Databricks Hive Spark SQL Machine Learning Experiment Design Model Deployment Azure C#
Skill cluster (5 dimension groups, role-scoped)
Programming Languages for ML Systems
Python C#
AI Governance and Model Security
Machine Learning
Cloud Platforms
Azure
MLOps Platforms and Lifecycle
Azure ML
Cross-cutting / unaligned
Deep Neural Network Azure Databricks Hive Spark SQL Experiment Design Model Deployment
Show KRA description ↓
• 3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets. • Proficiency in Python, Azure ML, or other statistics/ML tools. • Proficiency in Deep Neural Network, Python based frameworks. • Proficiency in Azure DataBricks, Hive, Spark. • Proficiency in deploying models into production (Azure stack). • Moderate coding skills. SQL or similar required. C# or other languages strongly preferred. • Outstanding communication and collaboration skills. You can learn from and teach others. • Strong drive for results. You have a proven record of shepherding experiments to create successful shipping products/services. • Understanding of the model development ecosystem across platforms, including development, distribution, and best practices, highly desirable. • Experience with prediction in adversarial (energy) environments highly desirable.

Signals

Skill ml-ops-engineer
0.36
Alias
KRA ml-ops-engineer
0.41

Post-classification

Centroidupdated · n=27
Alias collision log
New-role queue
New skills captured4
New KRA capturedyes

Captured for admin review

Deep Neural Network primary ML Engineer pending
Azure Databricks primary ML Engineer pending
Experiment Design primary ML Engineer pending
Model Deployment primary ML Engineer pending
R&R fragment (sim 0.36) ML Engineer pending

• 3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets. • Proficiency in Python, Azure ML, or other statistics/ML …

Status: completed Created: 2026-05-27T16:14:50.240036Z Updated: 2026-05-27T16:17:05.272054Z API 3 duration: 53687 ms
Flow Current 3-step pipeline

1 POST /skills/extract-from-jd

2 POST /skills/extract-details

3 POST /skills/final-role-output

Role Chosen role & resolution

ML Engineer

domain · AI / ML CASE DOMAIN

slug: ml-engineer · id: 3 · source: db

Domain=AI / ML; The JD focuses on applied machine learning, experiment design, model development, and deploying models into production, which best matches an ML Engineer role.

Matched skills

experiment designapplied machine learningPythonAzure MLDeep Neural NetworkPython based frameworksAzure DataBricksHiveSparkSQLC#Azure stack

Matched dimensions

Applied machine learningExperimental design and evaluationModel deployment to productionLarge-scale data processingCross-functional collaborationModel development lifecycle

Matched KRAs

design and applied machine learning predicting outcomesdeploying models into productionshepherding experiments to create successful shipping products/servicesunderstanding the model development ecosystem across platforms

Resolution: in_db — role exists in library; skill↔dim and role↔dim links saved when applicable.

0
New skills
0
Skill↔dim saved
0
Role↔dim saved
0
Skipped

Job description

About Energy Exemplar
In an era where the world is rapidly advancing towards a cleaner future through decarbonization, Energy Exemplar’s mission lies in ‘Empowering Transformative Energy Decisions’. Founded in 1999 in Adelaide, Australia, our award-winning software portfolio encompassing the modeling and simulation platform PLEXOS®, Aurora, and Adapt2, is trusted by innovative organizations across the globe. Through our technology and people, we strive to enable stakeholders from across the entire energy value chain to revolutionize the energy ecosystem and to collaboratively plan and execute for a sustainable energy future with unprecedented clarity, speed, and innovation. 
Our impact is global and is being recognized across the industry. Some of our recent accolades include:
• SaaS Company of the Year (2025) – Global Business Tech Awards.
• Environmental Impact Award (2025) – E+E Leaders Awards.
• IPPAI (Independent Power Producers Association of India) Power Awards (2025) - Winners
• Finalist: Platts Global Energy Awards (2024) – Grid Edge category
• Finalist: Reuters Global Energy Transition Awards (2024) – Technologies of Change
• Top 50 Marketing Team (2024) – Voted by the public at the ICON Awards.
How We Work
Energy Exemplar is growing fast around 30% year on year and, that growth is driven by how we work. We trust our team to deliver great results from wherever they work best, whether that’s at home, in the office, or on the move.
We’re a global team that values ownership, integrity, and innovation. You’ll be supported to balance work and life in a way that works for you, and empowered to take initiative, solve problems, and make an impact, regardless of your background, location, or role.
Our four core values, Customer Success, One Global Team, Integrity & Ownership, and Innovation Excellence aren’t just words. They show up in how we collaborate, how we solve, and how we grow together.
About Energy Exemplar
In an era where the world is rapidly advancing towards a cleaner future through decarbonization, stakeholders from across the entire energy value chain are having to navigate the complexities of the energy ecosystem. We seek to enable our customers to do so with confidence. 
Our mission: Empowering Transformative Energy Decisions.
Founded in 1999 in Adelaide, Australia, Energy Exemplar’s award-winning PLEXOS® modeling and simulation software is trusted by innovative organizations across the globe. On one unified platform, stakeholders from across the entire energy value chain are revolutionizing the energy ecosystem and seamlessly planning for the future of energy with unprecedented clarity, speed, and innovation.
Our impact is global and is being recognized across the industry:
• Finalist for the 2024 Reuters Global Energy Transition Awards in the 'Technologies of Change' category 
• Finalist for the 2024 Go:Tech Awards in the 'Most Innovative Use of Technology’ category
• 2022 USEA/USAID Corporate Volunteer of the Year  
• 2022 Impact Award Winner for our impact on the energy industry and the current energy transition 
Energy Exemplar has grown significantly over the past few years, and we are continuing to do so at around 30% year on year. We don’t just celebrate the excellence of our products but champion the quality of our people. They own their outcomes and perform to their best – every day. That’s what makes us who we are and a great place to work. Our core values ‘Customer Success’, ‘One Global Team’, ‘Integrity and Ownership’ and ‘Innovation Excellence’ reflect the way we work and are always at the forefront of everything we do.
 

Candidate Requirements & Qualifications

• 3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.
• Proficiency in Python, Azure ML, or other statistics/ML tools.
• Proficiency in Deep Neural Network, Python based frameworks.
• Proficiency in Azure DataBricks, Hive, Spark.
• Proficiency in deploying models into production (Azure stack).
• Moderate coding skills. SQL or similar required. C# or other languages strongly preferred.
• Outstanding communication and collaboration skills. You can learn from and teach others.
• Strong drive for results. You have a proven record of shepherding experiments to create successful shipping products/services.
• Understanding of the model development ecosystem across platforms, including development, distribution, and best practices, highly desirable


Desired but not required (not needed for every position)
• Experience with prediction in adversarial (energy) environments highly desirable.



Energy Exemplar is an equal opportunities employer and we value your unique identity and perspective. We are fully committed to providing and fostering a workplace that reflects the diversity of society. Bring your authentic self and help us build an inclusive world together! To support you in being the best version of yourself during the application and interview process, please let us know if you have any specific requirements.
Energy Exemplar is proud to be an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all team members. We welcome applications from people of all backgrounds, experiences, identities, and abilities. Please let us know if you require accommodations at any stage of the recruitment process—we're here to support you in showcasing your full potential.
Energy Exemplar respects your privacy and is committed to protecting the personal data you share during the recruitment process. This Candidate Privacy Notice explains how we collect, use, and protect your personal information when you apply for a role with us.

Skills from this JD

Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.

Python Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Python id=5 · python

Aliases — catalog

  • Python (CANONICAL) primary
  • Python 2 (VERSION)
  • Python 2.x (VERSION)
  • Python 3 (VERSION)
  • Python 3.10 (VERSION)
  • Python 3.11 (VERSION)
  • Python 3.12 (VERSION)
  • Python 3.x (VERSION)
  • py (VERSION)
  • py2 (VERSION)
  • py3 (VERSION)
  • python 3 (VERSION)
  • python 3.x (VERSION)
  • python2 (VERSION)
  • python3 (VERSION)
  • python3.x (VERSION)

Context tags (catalog)

API Django FastAPI Flask Jupyter NumPy PEP 8 Pandas REST SQLAlchemy asyncio pandas pip pytest type hints venv virtualenv

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
PSF
License
mit
Year introduced
1991
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
3

Maturity reasoning: Python appears in a very high volume of job descriptions across data, backend, automation, and ML roles, and remains a default hiring-pipeline language on major job boards and tech stacks.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Security Scripting & DSL Languages Catalog dimension db id 248

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages and Scripting Catalog dimension db id 59

    Library dimension (catalog)

    Roles linked in library: Cyber Security Engineer

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • Python Programming Catalog dimension db id 290

    Library dimension (catalog)

    Roles linked in library: Python Backend Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension saved
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure ML Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure ML id=212 · azure-ml

Aliases — catalog

  • Azure ML (CANONICAL) primary

Context tags (catalog)

AKS AutoML Azure Databricks Azure DevOps Azure Functions Azure Machine Learning ML Studio MLflow REST API SDK v2 TensorFlow automated ML compute cluster compute instance data labeling data preprocessing datastore designer endpoint deployment feature store hyperparameter tuning model deployment model monitoring model registry notebooks pipeline orchestration pipelines scikit-learn workspace

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Ml Platform
Vendor
Microsoft
License
proprietary
Year introduced
2018
Confidence
0.97
Version strategy
NOT_APPLICABLE

Maturity reasoning: Azure ML appears frequently in ML/DS job postings and Microsoft’s Azure AI portfolio, indicating broad enterprise adoption for model training and deployment on Azure.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
175
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • MLOps Platforms and Lifecycle Catalog dimension db id 43

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
MLOps Platforms and Lifecycle
mlops-platforms-and-lifecycle
Existing dimension (library) · Role↔dimension saved
Deep Neural Network Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
CONCEPT
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Azure Databricks Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Cloud Platforms
Sub-category
Data Engineering Tools
Skill nature
PLATFORM
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Hive Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Hive id=2754 · hive

Aliases — catalog

  • Hive (CANONICAL) primary

Context tags (catalog)

Apache Apache Hive Bucketing ETL HQL Hive Metastore Hive SerDe HiveQL MapReduce SQL SQL-on-Hadoop big data bucketing columnar storage data lakes data warehousing integration metadata partitioning schema evolution

Stored enrichment (catalog DB)

Category
Datastore
Sub-category
Local Key Value Store
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.90
Version strategy
NOT_APPLICABLE

Maturity reasoning: Hive appears in Flutter/mobile JDs and package docs, but JD volume is far below SQLite/Realm and it’s mainly used for local key-value storage in Flutter apps.

Skill profile (library / DB)

Skill nature
TOOL
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
3
Sub-category id
2242
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Local Persistence and Offline Behavior Catalog dimension db id 85

    Library dimension (catalog)

    Roles linked in library: Android Developer, Flutter Developer, Hybrid Mobile Developer, Native Mobile Developer, React Native Developer, iOS Developer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Spark Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Apache Spark id=1350 · apache-spark

Aliases — catalog

  • Apache Spark (CANONICAL)
  • apache spark 3 (VERSION)
  • spark (VERSION)
  • spark 3 (VERSION)
  • spark 3.x (VERSION)
  • spark3 (VERSION)

Context tags (catalog)

Apache Kafka Cluster Manager DAGScheduler Data Lake DataFrame ETL Hadoop MLlib Machine Learning PySpark RDD Scala Spark SQL Spark Streaming SparkSession

Stored enrichment (catalog DB)

Category
Framework
Sub-category
Distributed Data Processing Framework
Vendor
Apache Software Foundation
License
apache_2
Year introduced
2010
Confidence
0.94
Version strategy
SEPARATE_ENTITY
Version tag
3.x

Maturity reasoning: Apache Spark appears in many data engineering JDs and remains a standard for distributed ETL/ELT; its GitHub and vendor ecosystem activity stay strong, with Databricks and cloud platforms still promoting it.

Skill profile (library / DB)

Skill nature
FRAMEWORK
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
5
Sub-category id
1021
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • ETL and ELT Tooling Catalog dimension db id 24

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: SQL id=101 · sql

Aliases — catalog

  • SQL (CANONICAL) primary

Context tags (catalog)

ACID CTE DDL DML ETL JOIN MySQL NoSQL OLAP ORM PostgreSQL SQL injection SQLite T-SQL data modeling data warehousing database normalization execution plan indexing joins normalization query optimization stored procedures subquery transaction isolation transaction management window functions

Stored enrichment (catalog DB)

Category
Language
Sub-category
Query Language
Vendor
ANSI
License
unknown
Year introduced
1974
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: SQL appears in a large share of data, backend, and analytics job descriptions and remains the default query language for PostgreSQL, MySQL, and cloud warehouses like Snowflake/BigQuery.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
97
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Pega Programming Languages & DSLs Catalog dimension db id 267

    Library dimension (catalog)

    Roles linked in library: Pega Developer

  • Programming Languages & DSLs Catalog dimension db id 475

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

  • Programming Languages for Data Work Catalog dimension db id 21

    Library dimension (catalog)

    Roles linked in library: Data Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# Secondary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: C# id=4 · c

Aliases — catalog

  • C# (CANONICAL) primary
  • C (CANONICAL)
  • C# 1 (VERSION)
  • C# 10 (VERSION)
  • C# 11 (VERSION)
  • C# 12 (VERSION)
  • C# 13 (VERSION)
  • C# 14 (VERSION)
  • C# 2 (VERSION)
  • C# 3 (VERSION)
  • C# 4 (VERSION)
  • C# 5 (VERSION)
  • C# 6 (VERSION)
  • C# 7 (VERSION)
  • C# 8 (VERSION)
  • C# 9 (VERSION)
  • C# latest (VERSION)
  • C#1 (VERSION)
  • C#10 (VERSION)
  • C#11 (VERSION)
  • C#12 (VERSION)
  • C#2 (VERSION)
  • C#3 (VERSION)
  • C#4 (VERSION)
  • C#5 (VERSION)
  • C#6 (VERSION)
  • C#7 (VERSION)
  • C#8 (VERSION)
  • C#9 (VERSION)
  • C++ (CANONICAL)
  • C++03 (VERSION)
  • C++11 (VERSION)
  • C++14 (VERSION)
  • C++17 (VERSION)
  • C++20 (VERSION)
  • C++23 (VERSION)
  • C++26 (VERSION)
  • C++98 (VERSION)
  • c sharp (VERSION)
  • c# (VERSION)
  • cpp03 (VERSION)
  • cpp11 (VERSION)
  • cpp14 (VERSION)
  • cpp17 (VERSION)
  • cpp20 (VERSION)
  • cpp23 (VERSION)
  • cpp26 (VERSION)
  • cpp98 (VERSION)
  • csharp (VERSION)
  • modern C++ (VERSION)

Context tags (catalog)

.NET ASP.NET Azure Boost C# 9 C11 C99 CLR CMake Entity Framework LINQ MSBuild MVC Makefile NuGet Qt RAII Roslyn STL TDD Unity Visual Studio WPF Web API WinForms Xamarin async/await attributes buffer overflow constexpr delegates dependency injection dynamic memory events free function pointer gcc gdb header files inline interfaces linker malloc memory leak memory management move semantics multithreading operator overloading pointer pointers preprocessor segmentation fault smart pointers stdio.h stdlib.h struct syntax error templates typedef xUnit

Stored enrichment (catalog DB)

Category
Language
Sub-category
Programming Language
Vendor
Microsoft
License
mit
Year introduced
2000
Confidence
0.99
Version strategy
SEPARATE_ENTITY
Version tag
latest

Maturity reasoning: C# is a mainstream hiring staple with high JD volume across .NET, Azure, and enterprise roles; Microsoft continues active platform investment in .NET, reinforcing broad adoption.

Skill profile (library / DB)

Skill nature
LANGUAGE
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
6
Sub-category id
96
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • C# and .NET Languages Catalog dimension db id 362

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer

  • Cross-Platform App Languages Catalog dimension db id 167

    Library dimension (catalog)

    Roles linked in library: Hybrid Mobile Developer

  • Programming Languages Catalog dimension db id 1

    Library dimension (catalog)

    Roles linked in library: Backend Developer, Fullstack Developer, Fullstack Developer

  • Programming Languages for ML Systems Catalog dimension db id 39

    Library dimension (catalog)

    Roles linked in library: ML Engineer, MLOps Engineer

  • Programming Languages for XR Catalog dimension db id 97

    Library dimension (catalog)

    Roles linked in library: AR/VR Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

  • Sitecore Development Languages Catalog dimension db id 438

    Library dimension (catalog)

    Roles linked in library: Sitecore Dev

  • Video Codec Languages and DSLs Catalog dimension db id 225

    Library dimension (catalog)

    Roles linked in library: Video Codec Engineer

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
C# and .NET Languages
c-and-net-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cross-Platform App Languages
cross-platform-app-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension saved
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Sitecore Development Languages
sitecore-development-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Video Codec Languages and DSLs
video-codec-languages-and-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Machine Learning id=1356 · machine-learning

Aliases — catalog

  • Machine Learning (CANONICAL)

Context tags (catalog)

Keras PyTorch TensorFlow cross-validation data preprocessing ensemble methods feature engineering hyperparameter tuning model evaluation natural language processing neural networks reinforcement learning scikit-learn supervised learning unsupervised learning

Stored enrichment (catalog DB)

Category
Concept
Sub-category
Machine Learning
Confidence
0.98
Version strategy
NOT_APPLICABLE

Maturity reasoning: Machine Learning appears in large volumes of job descriptions across data, product, and platform roles, and major cloud vendors (AWS, Google Cloud, Azure) offer dedicated ML services and certifications, indicating broad adoption.

Skill profile (library / DB)

Skill nature
CONCEPT
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
2
Sub-category id
1024
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • AI Governance and Model Security Catalog dimension db id 50

    Library dimension (catalog)

    Roles linked in library: AI Engineer, ML Engineer, MLOps Engineer

  • React Frontend Development Catalog dimension db id 96

    Library dimension (catalog)

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension saved
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Experiment Design Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Model Deployment Primary New / orchestrated API 3: new canonical path (new) New / unmatched skill (orchestrated in API 2)

Skill enrichment (orchestrator / LLM)

No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).

Derived legacy fields
Category
Machine Learning Frameworks
Sub-category
general
Skill nature
PRACTICE
Volatility
MEDIUM
Typical lifespan
MULTI_YEAR
Version strategy
UNVERSIONED
Azure Primary Library skill API 3: existing canonical (in_db) Existing skill (matched library)
Canonical: Azure id=188 · azure

Aliases — catalog

  • Azure (CANONICAL) primary

Context tags (catalog)

AKS ARM templates App Service Azure AD Azure Active Directory Azure App Service Azure Blob Azure Blob Storage Azure Cognitive Services Azure Cosmos DB Azure DevOps Azure DevTest Labs Azure Functions Azure Kubernetes Service Azure Logic Apps Azure Monitor Azure Networking Azure Resource Manager Azure SQL Azure SQL Database Azure Security Center Azure Storage Azure Storage Explorer Azure Virtual Machines Bicep Blob Storage Cloud Services Cosmos DB Entra ID Functions Infrastructure as Code Key Vault Log Analytics Logic Apps Resource Groups Serverless Computing Service Bus Storage Account Terraform Virtual Machines

Stored enrichment (catalog DB)

Category
Platform
Sub-category
Cloud Platform
Vendor
Microsoft
License
proprietary
Year introduced
2010
Confidence
0.99
Version strategy
NOT_APPLICABLE

Maturity reasoning: Azure is broadly adopted and frequently appears in cloud/platform job descriptions alongside AWS and GCP; Microsoft’s ongoing enterprise investment and Azure certification demand signal strong hiring-pipeline relevance.

Skill profile (library / DB)

Skill nature
PLATFORM
Volatility
STABLE
Typical lifespan
EVERGREEN
Category id
9
Sub-category id
46
Extractable
True
Also category
False

Dimensions (API 2 worklist)

  • Cloud Platforms Catalog dimension db id 20

    Library dimension (catalog)

    Roles linked in library: .NET Backend Developer, Backend Developer, Cyber Security Engineer, Data Engineer, DevOps Engineer, Fullstack Developer, Go Backend Developer, Java Backend Developer, Kotlin Backend Developer, ML Engineer, MLOps Engineer, Node.js Backend Developer, Python Backend Developer, Scala Backend Developer

  • Cloud Platforms & Managed Services Catalog dimension db id 221

    Library dimension (catalog)

    Roles linked in library: Fullstack Developer, Go Backend Developer, Node.js Backend Developer

  • Cloud Platforms for AI Deployment Catalog dimension db id 211

    Library dimension (catalog)

    Roles linked in library: AI Engineer

  • Cloud Provider Platforms Catalog dimension db id 131

    Library dimension (catalog)

    Roles linked in library: Cloud Architect, Cloud Security Engineer

  • Cloud Security Posture Tools Catalog dimension db id 64

    Library dimension (catalog)

    Roles linked in library: Cloud Security Engineer, Cyber Security Engineer

  • Vendor Product Families Catalog dimension db id 477

    Library dimension (catalog)

    Roles linked in library: Engineering Manager

API 3 link attempts (this skill)

Dimension Skill↔dim Role↔dim Outcome
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Cloud Platforms & Managed Services
cloud-platforms-managed-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

All API 3 persistence rows

Same grid as the skill-extractor “Persistence items” table: one row per (skill × dimension) work item.

Skill Tag Dimension Skill↔dim Role↔dim Outcome Notes
Python in_db
Cloud Security Scripting & DSL Languages
cloud-security-scripting-dsl-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages and Scripting
programming-languages-and-scripting
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension saved
Python in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Python in_db
Python Programming
python-programming
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure ML in_db
MLOps Platforms and Lifecycle
mlops-platforms-and-lifecycle
Existing dimension (library) · Role↔dimension saved
Hive in_db
Local Persistence and Offline Behavior
local-persistence-and-offline-behavior
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Spark in_db
ETL and ELT Tooling
etl-and-elt-tooling
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Pega Programming Languages & DSLs
pega-programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages & DSLs
programming-languages-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
SQL in_db
Programming Languages for Data Work
programming-languages-for-data-work
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
C# and .NET Languages
c-and-net-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Cross-Platform App Languages
cross-platform-app-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Programming Languages
programming-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Programming Languages for ML Systems
programming-languages-for-ml-systems
Existing dimension (library) · Role↔dimension saved
C# in_db
Programming Languages for XR
programming-languages-for-xr
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Sitecore Development Languages
sitecore-development-languages
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
C# in_db
Video Codec Languages and DSLs
video-codec-languages-and-dsls
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Machine Learning in_db
AI Governance and Model Security
ai-governance-and-model-security
Existing dimension (library) · Role↔dimension saved
Machine Learning in_db
React Frontend Development
d_init_01
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Platforms
cloud-platforms
Existing dimension (library) · Role↔dimension saved
Azure in_db
Cloud Platforms & Managed Services
cloud-platforms-managed-services
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Platforms for AI Deployment
cloud-platforms-for-ai-deployment
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Provider Platforms
cloud-provider-platforms
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Cloud Security Posture Tools
cloud-security-posture-tools
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)
Azure in_db
Vendor Product Families
vendor-product-families
Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role)

Library artifacts (this run)

Kind Detail DB id
canonical_skill_proposed Deep Neural Network | type=Machine Learning Frameworks subtype=general nature=CONCEPT lifespan=MULTI_YEAR
canonical_skill_proposed Azure Databricks | type=Cloud Platforms subtype=Data Engineering Tools nature=PLATFORM lifespan=MULTI_YEAR
canonical_skill_proposed Experiment Design | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
canonical_skill_proposed Model Deployment | type=Machine Learning Frameworks subtype=general nature=PRACTICE lifespan=MULTI_YEAR
nano JD Parser — gpt-4.1-nano click to toggle
CompanyEnergy Exemplar
Experience3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.
DomainOther
JD type pass
Show raw JSON
{
  "JD_type": "pass",
  "about_company": {
    "source_marker": {
      "first_5_words": "In an era where the",
      "last_5_words": "clarity, speed, and innovation."
    },
    "text": "In an era where the world is rapidly advancing towards a cleaner future through decarbonization, Energy Exemplar\u2019s mission lies in \u2018Empowering Transformative Energy Decisions\u2019. Founded in 1999 in Adelaide, Australia, our award-winning software portfolio encompassing the modeling and simulation platform PLEXOS\u00ae, Aurora, and Adapt2, is trusted by innovative organizations across the globe. Through our technology and people, we strive to enable stakeholders from across the entire energy value chain to revolutionize the energy ecosystem and to collaboratively plan and execute for a sustainable energy future with unprecedented clarity, speed, and innovation.",
    "word_count": 84
  },
  "archetype_override_applied": true,
  "archetype_override_matched_skills": [
    "Python",
    "Hive",
    "Make",
    "production",
    "Databricks",
    "Machine Learning",
    "Azure",
    "Azure ML",
    "Role",
    "SQL",
    "Edge",
    "Models",
    "Location",
    "producers",
    "grid"
  ],
  "certifications": [],
  "company_name": "Energy Exemplar",
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [],
      "domain": "Other"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 3,
    "raw": "3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets."
  },
  "job_locations": [],
  "role": null,
  "role_aliases": [],
  "role_archetype": "Engineering",
  "roles_and_responsibilities": [
    {
      "bullet_count": 8,
      "heading": "Candidate Requirements \u0026 Qualifications",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 3+ years of professional experience",
        "last_5_words": "development, distribution, and best practices, highly desirable."
      },
      "text": "\u2022 3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.\n\u2022 Proficiency in Python, Azure ML, or other statistics/ML tools.\n\u2022 Proficiency in Deep Neural Network, Python based frameworks.\n\u2022 Proficiency in Azure DataBricks, Hive, Spark.\n\u2022 Proficiency in deploying models into production (Azure stack).\n\u2022 Moderate coding skills. SQL or similar required. C# or other languages strongly preferred.\n\u2022 Outstanding communication and collaboration skills. You can learn from and teach others.\n\u2022 Strong drive for results. You have a proven record of shepherding experiments to create successful shipping products/services.\n\u2022 Understanding of the model development ecosystem across platforms, including development, distribution, and best practices, highly desirable.",
      "word_count": 134
    },
    {
      "bullet_count": 1,
      "heading": "Desired but not required (not needed for every position)",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Experience with prediction in",
        "last_5_words": "environments highly desirable."
      },
      "text": "\u2022 Experience with prediction in adversarial (energy) environments highly desirable.",
      "word_count": 14
    }
  ],
  "urls": []
}
API 1 — extract-from-jd click to toggle
{
  "final_skills": [
    {
      "is_primary": true,
      "skill_name": "Python"
    },
    {
      "is_primary": true,
      "skill_name": "Azure ML"
    },
    {
      "is_primary": true,
      "skill_name": "Deep Neural Network"
    },
    {
      "is_primary": true,
      "skill_name": "Azure Databricks"
    },
    {
      "is_primary": true,
      "skill_name": "Hive"
    },
    {
      "is_primary": true,
      "skill_name": "Spark"
    },
    {
      "is_primary": true,
      "skill_name": "SQL"
    },
    {
      "is_primary": false,
      "skill_name": "C#"
    },
    {
      "is_primary": true,
      "skill_name": "Machine Learning"
    },
    {
      "is_primary": true,
      "skill_name": "Experiment Design"
    },
    {
      "is_primary": true,
      "skill_name": "Model Deployment"
    },
    {
      "is_primary": true,
      "skill_name": "Azure"
    }
  ],
  "jd_role": null,
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": {
      "source_marker": {
        "first_5_words": "In an era where the",
        "last_5_words": "clarity, speed, and innovation."
      },
      "text": "In an era where the world is rapidly advancing towards a cleaner future through decarbonization, Energy Exemplar\u2019s mission lies in \u2018Empowering Transformative Energy Decisions\u2019. Founded in 1999 in Adelaide, Australia, our award-winning software portfolio encompassing the modeling and simulation platform PLEXOS\u00ae, Aurora, and Adapt2, is trusted by innovative organizations across the globe. Through our technology and people, we strive to enable stakeholders from across the entire energy value chain to revolutionize the energy ecosystem and to collaboratively plan and execute for a sustainable energy future with unprecedented clarity, speed, and innovation.",
      "word_count": 84
    },
    "archetype_override_applied": true,
    "archetype_override_matched_skills": [
      "Python",
      "Hive",
      "Make",
      "production",
      "Databricks",
      "Machine Learning",
      "Azure",
      "Azure ML",
      "Role",
      "SQL",
      "Edge",
      "Models",
      "Location",
      "producers",
      "grid"
    ],
    "certifications": [],
    "company_name": "Energy Exemplar",
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [],
        "domain": "Other"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": 3,
      "raw": "3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets."
    },
    "job_locations": [],
    "role": null,
    "role_aliases": [],
    "role_archetype": "Engineering",
    "roles_and_responsibilities": [
      {
        "bullet_count": 8,
        "heading": "Candidate Requirements \u0026 Qualifications",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 3+ years of professional experience",
          "last_5_words": "development, distribution, and best practices, highly desirable."
        },
        "text": "\u2022 3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.\n\u2022 Proficiency in Python, Azure ML, or other statistics/ML tools.\n\u2022 Proficiency in Deep Neural Network, Python based frameworks.\n\u2022 Proficiency in Azure DataBricks, Hive, Spark.\n\u2022 Proficiency in deploying models into production (Azure stack).\n\u2022 Moderate coding skills. SQL or similar required. C# or other languages strongly preferred.\n\u2022 Outstanding communication and collaboration skills. You can learn from and teach others.\n\u2022 Strong drive for results. You have a proven record of shepherding experiments to create successful shipping products/services.\n\u2022 Understanding of the model development ecosystem across platforms, including development, distribution, and best practices, highly desirable.",
        "word_count": 134
      },
      {
        "bullet_count": 1,
        "heading": "Desired but not required (not needed for every position)",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Experience with prediction in",
          "last_5_words": "environments highly desirable."
        },
        "text": "\u2022 Experience with prediction in adversarial (energy) environments highly desirable.",
        "word_count": 14
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "1ec67ae0-ca43-4c79-a511-ae7186c85096",
  "stage3_signals": {
    "alias_found": false,
    "alias_match_roles": [],
    "kra_match_roles": [
      {
        "display_name": "MLOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Coordinates model promotion workflows across development, staging, and production environments including integration testing and data contract validation.",
            "sentence": "Understanding of the model development ecosystem across platforms, including development, distribution, and best practices, highly desirable.",
            "similarity": 0.5173
          },
          {
            "kra_text": "Maintains model versioning, experiment lineage, and artifact tracking using MLflow, DVC, or Weights \u0026 Biases for reproducibility and auditability.",
            "sentence": "3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.",
            "similarity": 0.3954
          },
          {
            "kra_text": "Orchestrates model serving deployments to production using Kubernetes, MLflow Model Registry, SageMaker, or Kubeflow Serving infrastructure.",
            "sentence": "You have a proven record of shepherding experiments to create successful shipping products/services.",
            "similarity": 0.3023
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 16,
        "score": 0.405,
        "slug": "ml-ops-engineer",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "kra_matches": [
          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
            "sentence": "Understanding of the model development ecosystem across platforms, including development, distribution, and best practices, highly desirable.",
            "similarity": 0.4789
          },
          {
            "kra_text": "Collaborates with development teams to improve build processes, reduce deployment friction, containerize applications, and adopt DevOps best practices.",
            "sentence": "Outstanding communication and collaboration skills.",
            "similarity": 0.3449
          },
          {
            "kra_text": "Provisions and manages cloud infrastructure on AWS, Azure, or GCP using Terraform or CloudFormation to enforce infrastructure-as-code standards.",
            "sentence": "C# or other languages strongly preferred.",
            "similarity": 0.2963
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 10,
        "score": 0.3734,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "Java Backend Developer",
        "kra_matches": [
          {
            "kra_text": "service contract collaboration",
            "sentence": "Outstanding communication and collaboration skills.",
            "similarity": 0.4179
          },
          {
            "kra_text": "persistence and data modeling",
            "sentence": "Understanding of the model development ecosystem across platforms, including development, distribution, and best practices, highly desirable.",
            "similarity": 0.3721
          },
          {
            "kra_text": "persistence and data modeling",
            "sentence": "3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.",
            "similarity": 0.323
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 79,
        "score": 0.371,
        "slug": "java-backend-developer",
        "total_count": null
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": [
          {
            "kra_text": "Translates product requirements into machine learning system specifications including feature definitions, model architecture choices, and success metric definitions.",
            "sentence": "3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.",
            "similarity": 0.4171
          },
          {
            "kra_text": "Manages model versioning, shadow deployments, A/B test rollouts, and safe rollback procedures using MLflow or SageMaker model registry.",
            "sentence": "Understanding of the model development ecosystem across platforms, including development, distribution, and best practices, highly desirable.",
            "similarity": 0.3988
          },
          {
            "kra_text": "Manages model versioning, shadow deployments, A/B test rollouts, and safe rollback procedures using MLflow or SageMaker model registry.",
            "sentence": "You have a proven record of shepherding experiments to create successful shipping products/services.",
            "similarity": 0.2693
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 3,
        "score": 0.3617,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Fullstack Developer",
        "kra_matches": [
          {
            "kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
            "sentence": "Understanding of the model development ecosystem across platforms, including development, distribution, and best practices, highly desirable.",
            "similarity": 0.404
          },
          {
            "kra_text": "Designs and queries relational databases like PostgreSQL and document stores like MongoDB, writing migrations, indexes, and optimized queries.",
            "sentence": "3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.",
            "similarity": 0.3416
          },
          {
            "kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
            "sentence": "Outstanding communication and collaboration skills.",
            "similarity": 0.3305
          }
        ],
        "matched_count": null,
        "matched_skills": null,
        "role_id": 15,
        "score": 0.3587,
        "slug": "full-stack-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "MLOps Engineer",
        "kra_matches": null,
        "matched_count": 4,
        "matched_skills": [
          "Azure",
          "Azure ML",
          "Machine Learning",
          "Python"
        ],
        "role_id": 16,
        "score": 0.3636,
        "slug": "ml-ops-engineer",
        "total_count": 11
      },
      {
        "display_name": "ML Engineer",
        "kra_matches": null,
        "matched_count": 4,
        "matched_skills": [
          "Azure",
          "Azure ML",
          "Machine Learning",
          "Python"
        ],
        "role_id": 3,
        "score": 0.3636,
        "slug": "ml-engineer",
        "total_count": 11
      },
      {
        "display_name": "Data Engineer",
        "kra_matches": null,
        "matched_count": 4,
        "matched_skills": [
          "Apache Spark",
          "Azure",
          "Python",
          "SQL"
        ],
        "role_id": 2,
        "score": 0.3636,
        "slug": "data-engineer",
        "total_count": 11
      },
      {
        "display_name": "Engineering Manager",
        "kra_matches": null,
        "matched_count": 3,
        "matched_skills": [
          "Azure",
          "Python",
          "SQL"
        ],
        "role_id": 121,
        "score": 0.2727,
        "slug": "engineering-manager",
        "total_count": 11
      },
      {
        "display_name": "Cyber Security Engineer",
        "kra_matches": null,
        "matched_count": 2,
        "matched_skills": [
          "Azure",
          "Python"
        ],
        "role_id": 5,
        "score": 0.1818,
        "slug": "cybersecurity-engineer",
        "total_count": 11
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": false,
    "case": "DOMAIN",
    "chosen_role": {
      "display_name": "ML Engineer",
      "kra_matches": null,
      "matched_count": null,
      "matched_skills": null,
      "role_id": 3,
      "score": 0.97,
      "slug": "ml-engineer",
      "total_count": null
    },
    "confidence": 0.97,
    "is_new_role": false,
    "llm2_fired": false,
    "llm2_reasoning": null,
    "matched_dimensions": [
      "Applied machine learning",
      "Experimental design and evaluation",
      "Model deployment to production",
      "Large-scale data processing",
      "Cross-functional collaboration",
      "Model development lifecycle"
    ],
    "matched_kras": [
      "design and applied machine learning predicting outcomes",
      "deploying models into production",
      "shepherding experiments to create successful shipping products/services",
      "understanding the model development ecosystem across platforms"
    ],
    "matched_skills": [
      "experiment design",
      "applied machine learning",
      "Python",
      "Azure ML",
      "Deep Neural Network",
      "Python based frameworks",
      "Azure DataBricks",
      "Hive",
      "Spark",
      "SQL",
      "C#",
      "Azure stack"
    ],
    "new_role_display_name": null,
    "new_role_slug": null,
    "queued": false,
    "reasoning": "Domain=AI / ML; The JD focuses on applied machine learning, experiment design, model development, and deploying models into production, which best matches an ML Engineer role.",
    "sub_role": null
  },
  "stage5_updates": {
    "centroid_n_after": 27,
    "centroid_updated": true,
    "collision_log_id": null,
    "new_kra_attached": {
      "best_kra_similarity": 0.3617,
      "queue_id": 1406,
      "r_and_r_preview": "\u2022 3+ years of professional experience in experiment design and applied machine learning predicting outcomes in large-scale, complex datasets.\n\u2022 Proficiency in Python, Azure ML, or other statistics/ML ",
      "role_display_name": "ML Engineer",
      "role_slug": "ml-engineer",
      "status": "pending"
    },
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 19141,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Deep Neural Network",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 19142,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Azure Databricks",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 19143,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Experiment Design",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 19144,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Model Deployment",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}
API 2 — extract-details
{
  "alias_matches": [
    {
      "alias_persist_skipped_reason": "alias_text already exists for this canonical skill",
      "alias_persisted": false,
      "existing_alias_id": 67,
      "existing_alias_text": "Python",
      "input_term": "Python",
      "matched_canonical": {
        "category_id": 6,
        "display_name": "Python",
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          "id": 428,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 6,
        "display_name": "C#",
        "id": 4,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "LANGUAGE",
        "slug": "c",
        "sub_category_id": 96,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "C# and .NET Languages",
            "id": 362,
            "rationale": "Primary implementation languages and runtime features used to build backend service logic in the .NET ecosystem. This is the core coding surface for request handling, domain logic, and service internals.",
            "slug": "c-and-net-languages",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cross-Platform App Languages",
            "id": 167,
            "rationale": "Languages used to implement shared mobile features across iOS and Android from a common codebase. This is the primary coding surface for hybrid app logic, UI behavior, and platform-specific branching.",
            "slug": "cross-platform-app-languages",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Hybrid Mobile Developer",
              "id": 11,
              "rationale": null,
              "role_archetype": null,
              "slug": "hybrid-mobile-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages",
            "id": 1,
            "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
            "slug": "programming-languages",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 435,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "fullstack-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for ML Systems",
            "id": 39,
            "rationale": "Languages used to build training code, inference services, evaluation jobs, and ML glue code. This is the primary implementation surface for ML engineers across experimentation and productionization.",
            "slug": "programming-languages-for-ml-systems",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Programming Languages for XR",
            "id": 97,
            "rationale": "Primary implementation languages used to build immersive client features, interaction logic, and device-specific runtime behavior. This is the core coding surface for AR/VR experiences.",
            "slug": "programming-languages-for-xr",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AR/VR Engineer",
              "id": 8,
              "rationale": null,
              "role_archetype": null,
              "slug": "ar-vr-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": []
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Sitecore Development Languages",
            "id": 438,
            "rationale": "Core implementation languages and markup used to build Sitecore customizations, rendering logic, and site behavior. This is the primary authoring surface for Sitecore-specific code and templates.",
            "slug": "sitecore-development-languages",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Sitecore Dev",
              "id": 233,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "sitecore-dev",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Video Codec Languages and DSLs",
            "id": 225,
            "rationale": "Implementation languages and codec-specific description languages used to build compression, decoding, and transform logic. This is the primary coding surface for codec behavior and bitstream manipulation.",
            "slug": "video-codec-languages-and-dsls",
            "source": "db"
          },
          "input_skill": "C#",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Video Codec Engineer",
              "id": 22,
              "rationale": null,
              "role_archetype": null,
              "slug": "video-codec-engineer",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "C#",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Machine Learning",
          "alias_type": "CANONICAL",
          "id": 2015,
          "is_primary": false,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 2,
        "display_name": "Machine Learning",
        "id": 1356,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "CONCEPT",
        "slug": "machine-learning",
        "sub_category_id": 1024,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "AI Governance and Model Security",
            "id": 50,
            "rationale": "Controls and documentation used to make models safer, auditable, and compliant. ML engineers use this to manage model risk, supply chain integrity, and governance requirements.",
            "slug": "ai-governance-and-model-security",
            "source": "db"
          },
          "input_skill": "Machine Learning",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AI Engineer",
              "id": 13,
              "rationale": null,
              "role_archetype": null,
              "slug": "ai-engineer",
              "source": "db"
            },
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "React Frontend Development",
            "id": 96,
            "rationale": "Building interactive web user interfaces with React.js, including component composition, state management, hooks, and rendering patterns. React.js belongs here because it is a core library for client-side UI development in modern web applications.",
            "slug": "d_init_01",
            "source": "db"
          },
          "input_skill": "Machine Learning",
          "llm_role": null,
          "roles_from_db": []
        }
      ],
      "input_skill": "Machine Learning",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Experiment Design",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "experiment-design",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [],
      "canonical": null,
      "dimensions": [],
      "input_skill": "Model Deployment",
      "matched_via": null,
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": {
        "derived": {
          "category": "Machine Learning Frameworks",
          "skill_nature": "PRACTICE",
          "sub_category": "general",
          "typical_lifespan": "MULTI_YEAR",
          "version_strategy": "UNVERSIONED",
          "volatility": "MEDIUM"
        },
        "enrichment": null,
        "keep_log": [],
        "locked_dimensions": [],
        "merge_log": [],
        "placed": null,
        "relationships": null,
        "skill_id": "model-deployment",
        "split_log": [],
        "typed": null,
        "warnings": []
      },
      "source_tag": "llm",
      "was_in_llm_skills": true
    },
    {
      "aliases_in_db": [
        {
          "alias_text": "Azure",
          "alias_type": "CANONICAL",
          "id": 407,
          "is_primary": true,
          "match_strategy": "CASE_INSENSITIVE"
        }
      ],
      "canonical": {
        "category_id": 9,
        "display_name": "Azure",
        "id": 188,
        "is_also_category": false,
        "is_extractable": true,
        "skill_nature": "PLATFORM",
        "slug": "azure",
        "sub_category_id": 46,
        "typical_lifespan": "EVERGREEN",
        "volatility": "STABLE"
      },
      "dimensions": [
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Platforms",
            "id": 20,
            "rationale": "Underlying cloud providers that host the managed services or infrastructure used by the role, such as AWS, Azure, and GCP.",
            "slug": "cloud-platforms",
            "source": "db"
          },
          "input_skill": "Azure",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": ".NET Backend Developer",
              "id": 83,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "dotnet-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Backend Developer",
              "id": 1,
              "rationale": null,
              "role_archetype": "A Backend Engineer designs, builds, and maintains the server-side logic and data handling that power applications and services. They focus on implementing reliable business functionality, integrating with other systems, and ensuring the backend is scalable, maintainable, and observable.",
              "slug": "backend-engineer",
              "source": "db"
            },
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            },
            {
              "display_name": "Data Engineer",
              "id": 2,
              "rationale": null,
              "role_archetype": null,
              "slug": "data-engineer",
              "source": "db"
            },
            {
              "display_name": "DevOps Engineer",
              "id": 10,
              "rationale": null,
              "role_archetype": null,
              "slug": "devops-engineer",
              "source": "db"
            },
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Java Backend Developer",
              "id": 79,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "java-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Kotlin Backend Developer",
              "id": 84,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "kotlin-server-backend-developer",
              "source": "db"
            },
            {
              "display_name": "ML Engineer",
              "id": 3,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-engineer",
              "source": "db"
            },
            {
              "display_name": "MLOps Engineer",
              "id": 16,
              "rationale": null,
              "role_archetype": null,
              "slug": "ml-ops-engineer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Python Backend Developer",
              "id": 80,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "python-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Scala Backend Developer",
              "id": 87,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "scala-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Platforms \u0026 Managed Services",
            "id": 221,
            "rationale": "Operates and integrates vendor-specific cloud compute, storage, and hosting services.",
            "slug": "cloud-platforms-managed-services",
            "source": "db"
          },
          "input_skill": "Azure",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Fullstack Developer",
              "id": 15,
              "rationale": null,
              "role_archetype": null,
              "slug": "full-stack-engineer",
              "source": "db"
            },
            {
              "display_name": "Go Backend Developer",
              "id": 81,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "go-backend-developer",
              "source": "db"
            },
            {
              "display_name": "Node.js Backend Developer",
              "id": 82,
              "rationale": null,
              "role_archetype": "Engineering",
              "slug": "node-backend-developer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Platforms for AI Deployment",
            "id": 211,
            "rationale": "Major cloud services that provide infrastructure and managed services for AI workloads.",
            "slug": "cloud-platforms-for-ai-deployment",
            "source": "db"
          },
          "input_skill": "Azure",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "AI Engineer",
              "id": 13,
              "rationale": null,
              "role_archetype": null,
              "slug": "ai-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Provider Platforms",
            "id": 131,
            "rationale": "Major cloud platforms and their core service ecosystems used to design target-state architectures, choose deployment boundaries, and evaluate managed capabilities. This is the primary substrate for cloud architecture decisions.",
            "slug": "cloud-provider-platforms",
            "source": "db"
          },
          "input_skill": "Azure",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Architect",
              "id": 9,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-architect",
              "source": "db"
            },
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Cloud Security Posture Tools",
            "id": 64,
            "rationale": "Cloud-native security platforms used to assess misconfiguration, workload exposure, and cloud control coverage. This dimension includes the major CNAPP/CSPM/CWPP vendors and cloud security services the role reviews and tunes.",
            "slug": "cloud-security-posture-tools",
            "source": "db"
          },
          "input_skill": "Azure",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Cloud Security Engineer",
              "id": 23,
              "rationale": null,
              "role_archetype": null,
              "slug": "cloud-security-engineer",
              "source": "db"
            },
            {
              "display_name": "Cyber Security Engineer",
              "id": 5,
              "rationale": null,
              "role_archetype": null,
              "slug": "cybersecurity-engineer",
              "source": "db"
            }
          ]
        },
        {
          "dimension": {
            "difficulty_hint": "well_known",
            "display_name": "Vendor Product Families",
            "id": 477,
            "rationale": "Coordinate usage, licensing, and architecture decisions for major vendor software and cloud product families.",
            "slug": "vendor-product-families",
            "source": "db"
          },
          "input_skill": "Azure",
          "llm_role": null,
          "roles_from_db": [
            {
              "display_name": "Engineering Manager",
              "id": 121,
              "rationale": null,
              "role_archetype": null,
              "slug": "engineering-manager",
              "source": "db"
            }
          ]
        }
      ],
      "input_skill": "Azure",
      "matched_via": "alias",
      "new_alias_persisted": false,
      "new_alias_text": null,
      "new_skill_meta": null,
      "source_tag": "db",
      "was_in_llm_skills": true
    }
  ],
  "unmatched_skills": [
    "Deep Neural Network",
    "Azure Databricks",
    "Experiment Design",
    "Model Deployment"
  ]
}
API 3 — final-role-output
{
  "chosen_role": {
    "display_name": "ML Engineer",
    "id": 3,
    "rationale": "Domain=AI / ML; The JD focuses on applied machine learning, experiment design, model development, and deploying models into production, which best matches an ML Engineer role.",
    "role_archetype": null,
    "slug": "ml-engineer",
    "source": "db"
  },
  "chosen_role_resolution": "in_db",
  "final_input_skills": [
    {
      "skill": "Python",
      "tag": "in_db"
    },
    {
      "skill": "Azure ML",
      "tag": "in_db"
    },
    {
      "skill": "Deep Neural Network",
      "tag": "new"
    },
    {
      "skill": "Azure Databricks",
      "tag": "new"
    },
    {
      "skill": "Hive",
      "tag": "in_db"
    },
    {
      "skill": "Spark",
      "tag": "in_db"
    },
    {
      "skill": "SQL",
      "tag": "in_db"
    },
    {
      "skill": "C#",
      "tag": "in_db"
    },
    {
      "skill": "Machine Learning",
      "tag": "in_db"
    },
    {
      "skill": "Experiment Design",
      "tag": "new"
    },
    {
      "skill": "Model Deployment",
      "tag": "new"
    },
    {
      "skill": "Azure",
      "tag": "in_db"
    }
  ],
  "llm_cost_api1_usd": null,
  "llm_cost_api2_usd": null,
  "llm_cost_api3_usd": null,
  "llm_cost_total_usd": null,
  "persistence": {
    "items": [
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Cloud Security Scripting \u0026 DSL Languages",
          "id": 248,
          "rationale": "Proficiency in programming and domain-specific languages used to automate and script cloud security controls.",
          "slug": "cloud-security-scripting-dsl-languages",
          "source": "db"
        },
        "dimension_id": 248,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Cloud Security Engineer",
            "id": 23,
            "rationale": null,
            "role_archetype": null,
            "slug": "cloud-security-engineer",
            "source": "db"
          }
        ],
        "skill_dimension_saved": true,
        "skill_id": 5,
        "skill_tag": "in_db",
        "skipped_reason": null
      },
      {
        "chosen_role_id": 3,
        "dimension": {
          "difficulty_hint": "well_known",
          "display_name": "Programming Languages",
          "id": 1,
          "rationale": "Primary implementation languages used to build client and server feature code. Full stack engineers need enough fluency to move across layers and implement product behavior end to end.",
          "slug": "programming-languages",
          "source": "db"
        },
        "dimension_id": 1,
        "input_skill": "Python",
        "llm_role": null,
        "matched_chosen_role": false,
        "outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension skipped (dimension not under chosen role)",
        "role_dimension_saved": false,
        "roles_from_db": [
          {
            "display_name": "Backend Developer",
            "id": 1,
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