Pipeline run
f10c1cb4-fb5d-4632-81e1-a545c8696b63
Client output enrichment
v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA descriptionvocab breakdown (legacy)
Signals
Post-classification
Captured for admin review
1 POST /skills/extract-from-jd
2 POST /skills/extract-details
3 POST /skills/final-role-output
ML Engineer
CASE Aslug: ml-engineer · id: 3 · source: db
Exact alias hit on ml-engineer (1.0) — no other alias at this confidence; skill_top ml-engineer 0.50 does not contradict
Resolution:
in_db
— role exists in library; skill↔dim and role↔dim links saved when applicable.
Job description
Want to make every line of code count? Tired of being a small cog in a big machine? Like a fast-paced environment where stuff get DONE? Wanna grow with a fast-growing company (both career and compensation)? Like to wear different hats? Join ThinkDeeply in our mission to create and apply Enterprise-Grade AI for all types of applications. Seeking an M.L. Engineer with high aptitude toward development. Will also consider coders with high aptitude in M.L. Years of experience is important but we are also looking for interest and aptitude. As part of the early engineering team, you will have a chance to make a measurable impact in future of Thinkdeeply as well as having a significant amount of responsibility. Experience 7+ Years Location Hyderabad Required Skills • Bachelors/Masters or Phd in Computer Science or related industry experience • 3+ years of Industry/Production Experience in Deep Learning Frameworks in PyTorch or TensorFlow • 7+ Years of industry experience in scripting languages such as Python, R. • 1+ years of industry experience in setting up production grade pipelines in Kubeflow/MLFlow • 7+ years in software development doing at least some level of Researching / POCs, Prototyping, Productizing, Process improvement, Large-data processing / performance computing • Familiar with non-neural network methods such as Bayesian, SVM, Adaboost, Random Forests etc • Some experience in setting up large scale training data pipelines. • Some experience in production edge based models. • Some experience in using Cloud services such as AWS, GCP, Azure Desired Skills • Experience in building deep learning models for Computer Vision and Natural Language Processing domains • Experience in productionizing/serving machine learning in industry setting • Understand the principles of developing cloud native applications Responsibilities • Collect, Organize and Process data pipelines for developing ML models • Research and develop novel prototypes for customers • Train, implement and evaluate shippable machine learning models • Deploy and iterate improvements of ML Models through feedback
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
- 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, 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) |
Skill enrichment (orchestrator / LLM)
No Stage 7 enrichment blob on this skill (orchestrator skipped enrichment).
- Category
- Data Engineering Tools
- Sub-category
- general
- Skill nature
- CONCEPT
- Volatility
- MEDIUM
- Typical lifespan
- MULTI_YEAR
- Version strategy
- UNVERSIONED
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 |
|---|---|---|---|---|---|---|
| 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) |
Library artifacts (this run)
| Kind | Detail | DB id |
|---|---|---|
| canonical_skill_proposed | Data Pipelines | type=Data Engineering Tools subtype=general nature=CONCEPT lifespan=MULTI_YEAR |
nano JD Parser — gpt-4.1-nano click to toggle
Show raw JSON
{
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Want to make every line",
"last_5_words": "for all types of applications."
},
"text": "Want to make every line of code count? Tired of being a small cog in a big machine? Like a fast-paced environment where stuff get DONE? Wanna grow with a fast-growing company (both career and compensation)? Like to wear different hats? Join ThinkDeeply in our mission to create and apply Enterprise-Grade AI for all types of applications.",
"word_count": 56
},
"certifications": [],
"company_name": "ThinkDeeply",
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science (or related)",
"raw": "Bachelors/Masters or Phd in Computer Science or related industry experience",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 7,
"raw": "7+ Years"
},
"job_locations": [
{
"aliases": [],
"city": "Hyderabad",
"country": "India",
"state": null,
"work_mode": null
}
],
"role": "M.L. Engineer",
"role_aliases": [
"Machine Learning Engineer",
"ML Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 4,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Collect, Organize and Process",
"last_5_words": "improvements of ML Models through"
},
"text": "\u2022 Collect, Organize and Process data pipelines for developing ML models\n\u2022 Research and develop novel prototypes for customers\n\u2022 Train, implement and evaluate shippable machine learning models\n\u2022 Deploy and iterate improvements of ML Models through feedback",
"word_count": 41
}
],
"urls": []
}
API 1 — extract-from-jd click to toggle
{
"final_skills": [
{
"is_primary": true,
"skill_name": "Machine Learning"
},
{
"is_primary": true,
"skill_name": "Data Pipelines"
}
],
"jd_role": {
"display_name": "M.L. Engineer",
"rationale": null,
"role_aliases": [
"Machine Learning Engineer",
"ML Engineer"
],
"role_archetype": "Data",
"slug": ""
},
"nano_parsed": {
"JD_type": "pass",
"about_company": {
"source_marker": {
"first_5_words": "Want to make every line",
"last_5_words": "for all types of applications."
},
"text": "Want to make every line of code count? Tired of being a small cog in a big machine? Like a fast-paced environment where stuff get DONE? Wanna grow with a fast-growing company (both career and compensation)? Like to wear different hats? Join ThinkDeeply in our mission to create and apply Enterprise-Grade AI for all types of applications.",
"word_count": 56
},
"certifications": [],
"company_name": "ThinkDeeply",
"ctc": null,
"domain": {
"primary": {
"aliases": [],
"domain": "IT Services \u0026 Consulting"
},
"secondary": null
},
"education": [
{
"level": "Bachelor\u0027s",
"qualification": "BTECH/BE/BSC - Computer Science (or related)",
"raw": "Bachelors/Masters or Phd in Computer Science or related industry experience",
"requirement": "required"
}
],
"experience": {
"max": null,
"min": 7,
"raw": "7+ Years"
},
"job_locations": [
{
"aliases": [],
"city": "Hyderabad",
"country": "India",
"state": null,
"work_mode": null
}
],
"role": "M.L. Engineer",
"role_aliases": [
"Machine Learning Engineer",
"ML Engineer"
],
"role_archetype": "Data",
"roles_and_responsibilities": [
{
"bullet_count": 4,
"heading": "Responsibilities",
"heading_was_present": true,
"source_marker": {
"first_5_words": "\u2022 Collect, Organize and Process",
"last_5_words": "improvements of ML Models through"
},
"text": "\u2022 Collect, Organize and Process data pipelines for developing ML models\n\u2022 Research and develop novel prototypes for customers\n\u2022 Train, implement and evaluate shippable machine learning models\n\u2022 Deploy and iterate improvements of ML Models through feedback",
"word_count": 41
}
],
"urls": []
},
"rejected": false,
"rejection_reason": null,
"run_id": "f10c1cb4-fb5d-4632-81e1-a545c8696b63",
"stage3_signals": {
"alias_found": true,
"alias_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 1.0,
"slug": "ml-engineer",
"total_count": null
}
],
"kra_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": [
{
"kra_text": "Manages model versioning, shadow deployments, A/B test rollouts, and safe rollback procedures using MLflow or SageMaker model registry.",
"sentence": "Deploy and iterate improvements of ML Models through feedback",
"similarity": 0.5781
},
{
"kra_text": "Prepares, cleans, and transforms training datasets, manages feature stores, and builds feature engineering pipelines for model training.",
"sentence": "Collect, Organize and Process data pipelines for developing ML models",
"similarity": 0.572
},
{
"kra_text": "Designs end-to-end ML training pipelines and model inference workflows using TensorFlow, PyTorch, or scikit-learn on cloud ML platforms.",
"sentence": "Train, implement and evaluate shippable machine learning models",
"similarity": 0.5393
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 0.5631,
"slug": "ml-engineer",
"total_count": null
},
{
"display_name": "MLOps Engineer",
"kra_matches": [
{
"kra_text": "Sets up model monitoring dashboards, data drift detection, prediction performance tracking, and alert routing for production ML systems.",
"sentence": "Collect, Organize and Process data pipelines for developing ML models",
"similarity": 0.5714
},
{
"kra_text": "Manages the end-to-end ML model release lifecycle from training job completion through validation gates to production deployment approval.",
"sentence": "Deploy and iterate improvements of ML Models through feedback",
"similarity": 0.5694
},
{
"kra_text": "Manages the end-to-end ML model release lifecycle from training job completion through validation gates to production deployment approval.",
"sentence": "Train, implement and evaluate shippable machine learning models",
"similarity": 0.5295
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 16,
"score": 0.5568,
"slug": "ml-ops-engineer",
"total_count": null
},
{
"display_name": "AI Engineer",
"kra_matches": [
{
"kra_text": "Designs and implements prompt engineering workflows, few-shot examples, chain-of-thought patterns, and structured output parsing for AI feature pipelines.",
"sentence": "Collect, Organize and Process data pipelines for developing ML models",
"similarity": 0.5112
},
{
"kra_text": "Defines evaluation frameworks, automated test suites, and human feedback loops to measure AI feature quality, accuracy, and consistency.",
"sentence": "Deploy and iterate improvements of ML Models through feedback",
"similarity": 0.5096
},
{
"kra_text": "Defines evaluation frameworks, automated test suites, and human feedback loops to measure AI feature quality, accuracy, and consistency.",
"sentence": "Train, implement and evaluate shippable machine learning models",
"similarity": 0.4129
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 13,
"score": 0.4779,
"slug": "ai-engineer",
"total_count": null
},
{
"display_name": "Data Engineer",
"kra_matches": [
{
"kra_text": "Builds data ingestion pipelines to collect data from transactional databases, third-party APIs, event streams, and file sources into centralized data platforms.",
"sentence": "Collect, Organize and Process data pipelines for developing ML models",
"similarity": 0.6069
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Train, implement and evaluate shippable machine learning models",
"similarity": 0.4219
},
{
"kra_text": "Works with data analysts, data scientists, and business stakeholders to define data models, ingestion schedules, and data delivery requirements.",
"sentence": "Deploy and iterate improvements of ML Models through feedback",
"similarity": 0.3524
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 2,
"score": 0.4604,
"slug": "data-engineer",
"total_count": null
},
{
"display_name": "Fullstack Developer",
"kra_matches": [
{
"kra_text": "Works closely with product managers and UX designers to translate requirements and wireframes into working software features through iterative development.",
"sentence": "Research and develop novel prototypes for customers",
"similarity": 0.4538
},
{
"kra_text": "Delivers features through CI/CD pipelines using automated tests, staged rollouts, feature flags, and incremental deployments.",
"sentence": "Deploy and iterate improvements of ML Models through feedback",
"similarity": 0.4503
},
{
"kra_text": "Implements complete product features end-to-end from database schema design through backend API to frontend UI using JavaScript, TypeScript, Python, or Ruby on Rails.",
"sentence": "Train, implement and evaluate shippable machine learning models",
"similarity": 0.3946
}
],
"matched_count": null,
"matched_skills": null,
"role_id": 15,
"score": 0.4329,
"slug": "full-stack-engineer",
"total_count": null
}
],
"skill_match_roles": [
{
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Machine Learning"
],
"role_id": 3,
"score": 0.5,
"slug": "ml-engineer",
"total_count": 2
},
{
"display_name": "AI Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Machine Learning"
],
"role_id": 13,
"score": 0.5,
"slug": "ai-engineer",
"total_count": 2
},
{
"display_name": "MLOps Engineer",
"kra_matches": null,
"matched_count": 1,
"matched_skills": [
"Machine Learning"
],
"role_id": 16,
"score": 0.5,
"slug": "ml-ops-engineer",
"total_count": 2
}
]
},
"stage4_decision": {
"alias_collision_detected": false,
"case": "A",
"chosen_role": {
"display_name": "ML Engineer",
"kra_matches": null,
"matched_count": null,
"matched_skills": null,
"role_id": 3,
"score": 1.0,
"slug": "ml-engineer",
"total_count": null
},
"confidence": 1.0,
"is_new_role": false,
"llm2_fired": false,
"llm2_reasoning": null,
"matched_dimensions": [],
"matched_kras": [],
"matched_skills": [],
"new_role_display_name": null,
"new_role_slug": null,
"queued": false,
"reasoning": "Exact alias hit on ml-engineer (1.0) \u2014 no other alias at this confidence; skill_top ml-engineer 0.50 does not contradict",
"sub_role": null
},
"stage5_updates": {
"centroid_n_after": 34,
"centroid_updated": true,
"collision_log_id": null,
"new_kra_attached": null,
"new_skills_attached": [
{
"is_primary": true,
"queue_id": 25297,
"role_display_name": "ML Engineer",
"role_slug": "ml-engineer",
"skill_name": "Data Pipelines",
"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": 2015,
"existing_alias_text": "Machine Learning",
"input_term": "Machine Learning",
"matched_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"
},
"matched_via": "alias"
}
],
"candidate_roles": [
{
"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"
}
],
"chosen_role": {
"display_name": "ML Engineer",
"id": 3,
"rationale": "Exact alias hit on ml-engineer (1.0) \u2014 no other alias at this confidence; skill_top ml-engineer 0.50 does not contradict",
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
"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_final_skills": [
"Machine Learning",
"Data Pipelines"
],
"input_llm_skills": [
"Machine Learning",
"Data Pipelines"
],
"new_aliases_persisted": 0,
"run_id": "f10c1cb4-fb5d-4632-81e1-a545c8696b63",
"skills_detail": [
{
"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": "Data Pipelines",
"matched_via": null,
"new_alias_persisted": false,
"new_alias_text": null,
"new_skill_meta": {
"derived": {
"category": "Data Engineering Tools",
"skill_nature": "CONCEPT",
"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": "data-pipelines",
"split_log": [],
"typed": null,
"warnings": []
},
"source_tag": "llm",
"was_in_llm_skills": true
}
],
"unmatched_skills": [
"Data Pipelines"
]
}
API 3 — final-role-output
{
"chosen_role": {
"display_name": "ML Engineer",
"id": 3,
"rationale": "Exact alias hit on ml-engineer (1.0) \u2014 no other alias at this confidence; skill_top ml-engineer 0.50 does not contradict",
"role_archetype": null,
"slug": "ml-engineer",
"source": "db"
},
"chosen_role_resolution": "in_db",
"final_input_skills": [
{
"skill": "Machine Learning",
"tag": "in_db"
},
{
"skill": "Data Pipelines",
"tag": "new"
}
],
"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": "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"
},
"dimension_id": 50,
"input_skill": "Machine Learning",
"llm_role": null,
"matched_chosen_role": true,
"outcome_line": "Existing dimension (library) \u00b7 Role\u2194dimension saved",
"role_dimension_saved": true,
"roles_from_db": [
{
"display_name": "AI Engineer",
"id": 13,
"rationale": null,
"role_archetype": null,
"slug": "ai-engineer",
"source": "db"
},
{
"display_name": "ML Engineer",
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