Pipeline run
ad0f1725-cb7d-4d58-a9c8-86a91be7003b
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
ML Ops Engineer
CASE Aslug: ml-ops-engineer · id: 16 · source: db
The primary skills align well with the responsibilities of an ML Ops Engineer.
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Hiring MLOps Engineers (JAX, Py Torch, Pallas/Triton) to help train the next generation of large language models at a leading frontier AI lab. Remote from India. $35–$45/hour USD, W-2 contract, 30–40 hrs/week weekdays. Para AI Labs is sourcing this role on behalf of Mercor — a well-established AI hiring platform — for a leading frontier AI lab's Gen AI team. You won't be labeling data. You'll be designing the technical problems, writing reference solutions, and building the rubrics that feed directly into how the next generation of LLMs reason about MLOps and ML systems. The essentials Pay: $35–$45/hour USD (strong rates for India-based MLOps work) Work mode: Fully remote from anywhere in India Commitment: 30–40 hrs/week, weekdays only Employment: W-2 via Cincinnatus LLC (the employer of record) Placement: A leading frontier AI lab's Gen AI team What you'll do Design challenging tasks across MLOps, ML infrastructure, and ML systems for use in model training Write accurate, well-structured reference solutions covering distributed training, training pipelines, and GPU kernel optimization Evaluate submissions from other engineers and provide clear, written technical feedback Develop rubrics and guidelines for assessing training pipeline design, distributed systems reasoning, and kernel-level optimization Collaborate with other ML subject matter experts to maintain consistency and accuracy across the training data Guide research and engineering teams toward closing specific knowledge gaps in ML framework internals What you need 2+ years of professional experience in ML Infrastructure, MLOps, or ML Systems Engineering at a recognized, top-tier organization Hands-on production experience with JAX and/or Py Torch at scale — real training workloads, not coursework or hobby projects Experience writing or optimizing custom GPU kernels with Pallas (JAX) or Triton Demonstrable career progression Strong written English — you can explain complex technical decisions clearly Reliable availability for at least 30 hours/week on weekdays Why this is worth your time Top-tier USD rates for remote India-based MLOps work Direct, measurable impact on a frontier AI lab's next-generation models Proper W-2 employment via Cincinnatus — no invoice chasing, no contractor tax surprises, no weekend work Work alongside senior ML engineers and researchers from across the global AI ecosystem About Para AI Labs Para AI Labs is the curated destination for high-quality AI training and evaluation roles from leading platforms including Mercor, Scale AI, Turing, and others.
Skills from this JD
Each row merges API 1 extraction, API 2 library match / v3 orchestration (dimensions + locked dims), and API 3 persistence tags.
Aliases — catalog
- MLOps (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Methodology
- Sub-category
- Mlops
- Confidence
- 0.93
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: MLOps appears in many job descriptions for ML/platform roles and is a standard practice in major cloud vendor docs (AWS, GCP, Azure) for CI/CD, model monitoring, and deployment.
Skill profile (library / DB)
- Skill nature
- METHODOLOGY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 8
- Sub-category id
- 906
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD for Machine Learning Catalog dimension db id 56
Library dimension (catalog)
Roles linked in library: ML Engineer
-
Data Lineage and Metadata Catalog dimension db id 28
Library dimension (catalog)
Roles linked in library: Data Engineer
-
Deployment Rollouts and Release Control Catalog dimension db id 51
Library dimension (catalog)
Roles linked in library: ML Engineer, ML Ops Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Data Lineage and Metadata
data-lineage-and-metadata
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
|
Deployment Rollouts and Release Control
deployment-rollouts-and-release-control
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- Machine Learning (CANONICAL)
Context tags (catalog)
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, ML Ops 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) |
Aliases — catalog
- Distributed Training (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Distributed Training
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in ML/AI job descriptions for large-model work; major vendors like AWS, Google Cloud, and NVIDIA document distributed training as a standard production pattern.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1028
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Training Pipelines (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Architecture
- Sub-category
- Ml Training Pipeline Architecture
- Confidence
- 0.88
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common in ML job postings and platform docs; many JDs for MLOps/ML Engineer explicitly require building training pipelines in Airflow/Kubeflow/Vertex AI/SageMaker.
Skill profile (library / DB)
- Skill nature
- PATTERN
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 1
- Sub-category id
- 1029
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
CI/CD Pipeline Platforms Catalog dimension db id 150
Library dimension (catalog)
Roles linked in library: DevOps Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- GPU Kernel Optimization (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Gpu Kernel Optimization
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Widely listed in ML/HPC job descriptions and supported by major vendor docs (NVIDIA CUDA, ROCm) as a core performance skill for production inference/training.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1030
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Model Optimization and Acceleration Catalog dimension db id 53
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Model Optimization and Acceleration
model-optimization-and-acceleration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Distributed Systems (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Distributed Systems
- Confidence
- 0.98
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Common hiring requirement in backend/platform JDs at large tech firms; appears across AWS, Kafka, microservices, and systems roles, with strong GitHub/Stack Overflow activity and no sunset signal.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1035
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Cloud Platforms Catalog dimension db id 20
Library dimension (catalog)
Roles linked in library: Backend Engineer, Cybersecurity Engineer, Data Engineer, DevOps Engineer, Full Stack Engineer, ML Engineer, ML Ops Engineer
-
Performance and Scalability Tuning Catalog dimension db id 11
Library dimension (catalog)
Roles linked in library: Backend Engineer
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Performance and Scalability Tuning
performance-and-scalability-tuning
|
✓ | — | 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) |
Aliases — catalog
- ML Framework Internals (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Concept
- Sub-category
- Ml Framework Internals
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Job postings rarely ask for ML framework internals explicitly; it appears mostly in specialized roles at framework vendors or infra teams, while mainstream JDs focus on using PyTorch/TensorFlow rather than their internals.
Skill profile (library / DB)
- Skill nature
- CONCEPT
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 2
- Sub-category id
- 1034
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
React Frontend Development Catalog dimension db id 96
Library dimension (catalog)
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- JAX (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Machine Learning Library
- Vendor
- License
- apache_2
- Year introduced
- 2018
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: JAX appears increasingly in ML/AI job postings and research codebases, especially for high-performance training and TPU/GPU workloads, but it is not yet a universal hiring staple like PyTorch or TensorFlow.
Skill profile (library / DB)
- Skill nature
- LIBRARY
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 7
- Sub-category id
- 156
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, ML Ops Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
Aliases — catalog
- PyTorch (CANONICAL) primary
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Library
- Sub-category
- Machine Learning Library
- Vendor
- Meta
- License
- bsd
- Year introduced
- 2016
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: PyTorch appears in a large volume of ML/AI job descriptions and is a standard framework in research and production, alongside TensorFlow and CUDA ecosystems.
Skill profile (library / DB)
- Skill nature
- LIBRARY
- Volatility
- STABLE
- Typical lifespan
- EVERGREEN
- Category id
- 7
- Sub-category id
- 156
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
ML Frameworks and Libraries Catalog dimension db id 40
Library dimension (catalog)
Roles linked in library: ML Engineer, ML Ops Engineer
-
Model Fine-Tuning & Adaptation Catalog dimension db id 212
Library dimension (catalog)
Roles linked in library: AI Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved |
|
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Pallas (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Framework
- Sub-category
- Machine Learning Framework
- Vendor
- Pallas AI
- License
- apache_2
- Year introduced
- 2021
- Confidence
- 0.62
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Pallas is a newer JAX/TPU kernel framework from Google; it appears in niche ML systems discussions and docs, but JD volume is still low versus established accelerators like CUDA/XLA.
Skill profile (library / DB)
- Skill nature
- FRAMEWORK
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 5
- Sub-category id
- 147
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Model Optimization and Acceleration Catalog dimension db id 53
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Model Optimization and Acceleration
model-optimization-and-acceleration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Aliases — catalog
- Triton (CANONICAL)
Context tags (catalog)
Stored enrichment (catalog DB)
- Category
- Language
- Sub-category
- Gpu Kernel Language
- Vendor
- NVIDIA
- License
- other_open
- Year introduced
- 2015
- Confidence
- 0.90
- Version strategy
- NOT_APPLICABLE
Maturity reasoning: Triton appears in growing ML systems job postings and is increasingly used in open-source GPU kernel repos, but it is still far from a universal hiring staple like CUDA.
Skill profile (library / DB)
- Skill nature
- LANGUAGE
- Volatility
- EMERGING
- Typical lifespan
- EVERGREEN
- Category id
- 6
- Sub-category id
- 1033
- Extractable
- True
- Also category
- False
Dimensions (API 2 worklist)
-
Model Optimization and Acceleration Catalog dimension db id 53
Library dimension (catalog)
Roles linked in library: ML Engineer
API 3 link attempts (this skill)
| Dimension | Skill↔dim | Role↔dim | Outcome |
|---|---|---|---|
|
Model Optimization and Acceleration
model-optimization-and-acceleration
|
✓ | — | 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 |
|---|---|---|---|---|---|---|
| MLOps | in_db |
CI/CD for Machine Learning
ci-cd-for-machine-learning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| MLOps | in_db |
Data Lineage and Metadata
data-lineage-and-metadata
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| MLOps | in_db |
Deployment Rollouts and Release Control
deployment-rollouts-and-release-control
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| 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) | |
| Distributed Training | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Training Pipelines | in_db |
CI/CD Pipeline Platforms
ci-cd-pipeline-platforms
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| GPU Kernel Optimization | in_db |
Model Optimization and Acceleration
model-optimization-and-acceleration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Distributed Systems | in_db |
Cloud Platforms
cloud-platforms
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| Distributed Systems | in_db |
Performance and Scalability Tuning
performance-and-scalability-tuning
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Distributed Systems | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| ML Framework Internals | in_db |
React Frontend Development
d_init_01
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| JAX | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| PyTorch | in_db |
ML Frameworks and Libraries
ml-frameworks-and-libraries
|
✓ | ✓ | Existing dimension (library) · Role↔dimension saved | |
| PyTorch | in_db |
Model Fine-Tuning & Adaptation
model-fine-tuning-adaptation
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Pallas | in_db |
Model Optimization and Acceleration
model-optimization-and-acceleration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) | |
| Triton | in_db |
Model Optimization and Acceleration
model-optimization-and-acceleration
|
✓ | — | Existing dimension (library) · Role↔dimension skipped (dimension not under chosen role) |
Library artifacts (this run)
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Para AI Labs is the",
"last_5_words": "including Mercor, Scale AI, Turing"
},
"text": "Para AI Labs is the curated destination for high-quality AI training and evaluation roles from leading platforms including Mercor, Scale AI, Turing, and others.",
"word_count": 27
},
"certifications": [],
"company_name": "Mercor",
"ctc": {
"currency": "USD",
"max": 45,
"min": 35,
"period": "hourly",
"raw": "$35\u2013$45/hour USD"
},
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 2,
"raw": "2+ years of professional experience"
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": "India",
"state": null,
"work_mode": "remote"
}
],
"role": "MLOps Engineer",
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "What you\u0027ll do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Design challenging tasks across MLOps,",
"last_5_words": "gaps in ML framework internals"
},
"text": "Design challenging tasks across MLOps, ML infrastructure, and ML systems for use in model training\nWrite accurate, well-structured reference solutions covering distributed training, training pipelines, and GPU kernel optimization\nEvaluate submissions from other engineers and provide clear, written technical feedback\nDevelop rubrics and guidelines for assessing training pipeline design, distributed systems reasoning, and kernel-level optimization\nCollaborate with other ML subject matter experts to maintain consistency and accuracy across the training data\nGuide research and engineering teams toward closing specific knowledge gaps in ML framework internals",
"word_count": 66
},
{
"bullet_count": 6,
"heading": "What you need",
"heading_was_present": true,
"source_marker": {
"first_5_words": "2+ years of professional experience",
"last_5_words": "on weekdays"
},
"text": "2+ years of professional experience in ML Infrastructure, MLOps, or ML Systems Engineering at a recognized, top-tier organization\nHands-on production experience with JAX and/or Py Torch at scale \u2014 real training workloads, not coursework or hobby projects\nExperience writing or optimizing custom GPU kernels with Pallas (JAX) or Triton\nDemonstrable career progression\nStrong written English \u2014 you can explain complex technical decisions clearly\nReliable availability for at least 30 hours/week on weekdays",
"word_count": 66
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "MLOps"
},
{
"is_primary": true,
"skill_name": "Machine Learning"
},
{
"is_primary": true,
"skill_name": "Distributed Training"
},
{
"is_primary": true,
"skill_name": "Training Pipelines"
},
{
"is_primary": true,
"skill_name": "GPU Kernel Optimization"
},
{
"is_primary": true,
"skill_name": "Distributed Systems"
},
{
"is_primary": true,
"skill_name": "ML Framework Internals"
},
{
"is_primary": true,
"skill_name": "JAX"
},
{
"is_primary": true,
"skill_name": "PyTorch"
},
{
"is_primary": true,
"skill_name": "Pallas"
},
{
"is_primary": true,
"skill_name": "Triton"
}
],
"jd_role": {
"display_name": "MLOps Engineer",
"rationale": null,
"role_archetype": "Engineering",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Para AI Labs is the",
"last_5_words": "including Mercor, Scale AI, Turing"
},
"text": "Para AI Labs is the curated destination for high-quality AI training and evaluation roles from leading platforms including Mercor, Scale AI, Turing, and others.",
"word_count": 27
},
"certifications": [],
"company_name": "Mercor",
"ctc": {
"currency": "USD",
"max": 45,
"min": 35,
"period": "hourly",
"raw": "$35\u2013$45/hour USD"
},
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [],
"experience": {
"max": null,
"min": 2,
"raw": "2+ years of professional experience"
},
"job_locations": [
{
"aliases": [],
"city": null,
"country": "India",
"state": null,
"work_mode": "remote"
}
],
"role": "MLOps Engineer",
"role_archetype": "Engineering",
"roles_and_responsibilities": [
{
"bullet_count": 6,
"heading": "What you\u0027ll do",
"heading_was_present": true,
"source_marker": {
"first_5_words": "Design challenging tasks across MLOps,",
"last_5_words": "gaps in ML framework internals"
},
"text": "Design challenging tasks across MLOps, ML infrastructure, and ML systems for use in model training\nWrite accurate, well-structured reference solutions covering distributed training, training pipelines, and GPU kernel optimization\nEvaluate submissions from other engineers and provide clear, written technical feedback\nDevelop rubrics and guidelines for assessing training pipeline design, distributed systems reasoning, and kernel-level optimization\nCollaborate with other ML subject matter experts to maintain consistency and accuracy across the training data\nGuide research and engineering teams toward closing specific knowledge gaps in ML framework internals",
"word_count": 66
},
{
"bullet_count": 6,
"heading": "What you need",
"heading_was_present": true,
"source_marker": {
"first_5_words": "2+ years of professional experience",
"last_5_words": "on weekdays"
},
"text": "2+ years of professional experience in ML Infrastructure, MLOps, or ML Systems Engineering at a recognized, top-tier organization\nHands-on production experience with JAX and/or Py Torch at scale \u2014 real training workloads, not coursework or hobby projects\nExperience writing or optimizing custom GPU kernels with Pallas (JAX) or Triton\nDemonstrable career progression\nStrong written English \u2014 you can explain complex technical decisions clearly\nReliable availability for at least 30 hours/week on weekdays",
"word_count": 66
}
],
"urls": []
},
"rejected": false,
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}
API 2 — extract-details
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"input_final_skills": [
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"input_llm_skills": [
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},
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]
},
{
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"source": "db"
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}
],
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"new_skill_meta": null,
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},
{
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{
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}
],
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"new_skill_meta": null,
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{
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]
}
],
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{
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{
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},
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}
]
}
],
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"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
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{
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"id": 2028,
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"match_strategy": "CASE_INSENSITIVE"
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],
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"volatility": "STABLE"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
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"source": "db"
},
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{
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"source": "db"
},
{
"display_name": "Cybersecurity Engineer",
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},
{
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{
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{
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{
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"source": "db"
}
]
},
{
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"source": "db"
},
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}
],
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"new_skill_meta": null,
"source_tag": "db",
"was_in_llm_skills": true
},
{
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},
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{
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"source": "db"
},
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}
],
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},
{
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{
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}
],
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"skill_nature": "LIBRARY",
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"volatility": "EMERGING"
},
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{
"dimension": {
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},
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"display_name": "ML Engineer",
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},
{
"display_name": "ML Ops Engineer",
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]
}
],
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},
{
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{
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],
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},
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{
"dimension": {
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},
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{
"display_name": "ML Engineer",
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{
"display_name": "ML Ops Engineer",
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}
]
},
{
"dimension": {
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{
"display_name": "AI Engineer",
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]
}
],
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"new_skill_meta": null,
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"was_in_llm_skills": true
},
{
"aliases_in_db": [
{
"alias_text": "Pallas",
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"id": 2025,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 5,
"display_name": "Pallas",
"id": 1366,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "FRAMEWORK",
"slug": "pallas",
"sub_category_id": 147,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Model Optimization and Acceleration",
"id": 53,
"rationale": "Techniques for improving inference latency, throughput, memory use, and training efficiency. ML engineers use these methods to meet production constraints without sacrificing too much quality.",
"slug": "model-optimization-and-acceleration",
"source": "db"
},
"input_skill": "Pallas",
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"roles_from_db": [
{
"display_name": "ML Engineer",
"id": 3,
"rationale": null,
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
}
]
}
],
"input_skill": "Pallas",
"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": "Triton",
"alias_type": "CANONICAL",
"id": 2026,
"is_primary": false,
"match_strategy": "CASE_INSENSITIVE"
}
],
"canonical": {
"category_id": 6,
"display_name": "Triton",
"id": 1367,
"is_also_category": false,
"is_extractable": true,
"skill_nature": "LANGUAGE",
"slug": "triton",
"sub_category_id": 1033,
"typical_lifespan": "EVERGREEN",
"volatility": "EMERGING"
},
"dimensions": [
{
"dimension": {
"difficulty_hint": "well_known",
"display_name": "Model Optimization and Acceleration",
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"rationale": "Techniques for improving inference latency, throughput, memory use, and training efficiency. ML engineers use these methods to meet production constraints without sacrificing too much quality.",
"slug": "model-optimization-and-acceleration",
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},
"input_skill": "Triton",
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"roles_from_db": [
{
"display_name": "ML Engineer",
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"role_archetype": null,
"slug": "ml-engineer",
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}
]
}
],
"input_skill": "Triton",
"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": []
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "ML Ops Engineer",
"id": 16,
"rationale": "The primary skills align well with the responsibilities of an ML Ops Engineer.",
"role_archetype": null,
"slug": "ml-ops-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "MLOps",
"tag": "in_db"
},
{
"skill": "Machine Learning",
"tag": "in_db"
},
{
"skill": "Distributed Training",
"tag": "in_db"
},
{
"skill": "Training Pipelines",
"tag": "in_db"
},
{
"skill": "GPU Kernel Optimization",
"tag": "in_db"
},
{
"skill": "Distributed Systems",
"tag": "in_db"
},
{
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LLM Calls
Every model call made for this run, in pipeline order. Click a card to see the model's response.