Pipeline run

b140db0a-27ea-4c0a-af6a-0542291a2e8a

Pipeline LLM cost (USD)

API 1: $0.0036 API 2: $0.0000 API 3: $0.0000 Total: $0.0036

Client output enrichment

v2 Skill cluster · Nature of work · AI index · Tech stack maturity · Evidence · KRA description

Nature of work · Data/ML Platform Engineering

Build and scale data/ML pipelines for AI-driven services, review and standardize production ML systems, and lead development of UEBA, anomaly, and threat detection use cases while communicating across technical and business teams.

""Develop and maintain scalable data pipelines for various AI/ML-driven data services""

Tech stack maturity

Modern Cloud Native

The role focuses on ML engineering with anomaly detection and ML systems, which typically aligns with contemporary cloud-oriented, production-grade machine learning stacks rather than legacy or bleeding-edge only setups.

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): LLMs, AI, ML, AI/ML, GenAI, Machine Learning

Evidence — skills matched in JD (11)

Machine Learning Data Pipelines Data Ingestion Data Transformation Data Integration ML Pipelines Feature Engineering ML Systems UEBA Anomaly Detection Threat Detection

Skill cluster (3 dimension groups, role-scoped)

AI Governance and Model Security

Machine Learning

Model Monitoring and Drift Detection

Anomaly Detection

Cross-cutting / unaligned

Data Pipelines Data Ingestion Data Transformation Data Integration ML Pipelines Feature Engineering ML Systems UEBA Threat Detection

Show KRA description ↓

• Develop and maintain scalable data pipelines for various AI/ML-driven data services, ensuring reliable data ingestion, transformation, and integration from various sources, as well as maintaining ML feature and deployment pipelines. • Ensure that data architectures and infrastructure can scale seamlessly as the data volume and complexity grow. • Oversee ML design reviews, create best practices, and develop playbooks for end-to-end ML systems in production. • Lead the development of UEBA, Anomaly detection, Threat detection etc., leveraging the available subject matter expertise and with independent research where required. • Work with a great deal of autonomy and be the technical thought leader in creating a forward-looking vision with clear direction. • Effectively communicate complex technical artifacts to both technical (engineers & scientists) and non-technical audiences. • Collaborate closely with cross-functional teams, including business stakeholders, to innovate and unlock new use cases for our customers driven by data intelligence.

Signals

Skill ml-engineer

0.18

Alias ml-engineer

1.00

KRA ml-engineer

0.45

Post-classification

Centroidupdated · n=13

Alias collision log#65

New-role queue—

New skills captured8

New KRA captured—

Captured for admin review

Data Pipelines primary ↔ ML Engineer pending

Data Ingestion primary ↔ ML Engineer pending

Data Transformation primary ↔ ML Engineer pending

Data Integration primary ↔ ML Engineer pending

ML Pipelines primary ↔ ML Engineer pending

Feature Engineering primary ↔ ML Engineer pending

UEBA primary ↔ ML Engineer pending

Threat Detection primary ↔ ML Engineer pending

Status: extract_from_jd_done Created: 2026-05-19T23:06:02.165823Z Updated: 2026-05-19T23:06:03.286068Z

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

No chosen role stored for this run.

Job description

Principal AI/ML Engineer

Job description
Position: Principal AI/ML Engineer
Exp: 6+ years
Base Location: Hyderabad, Telangana
Notice Period: Immediate to 30 Days


The Principal AI/ML Engineer will be responsible for architecting and building data and ML pipelines across Cybersecurity and IT compliance segment. The ideal candidate is expected to work hands-on and possess deep subject matter expertise in AI/ML infrastructure and platforms. This role is responsible for end-to-end AI/ML operations.


Job Requirements:


• Minimum of 6 years of industry experience as AI/ML Engineer or Data Scientist role, with hands-on development experience.
• Hands-on expertise in Time Series Analysis, behavior analytics, and real-time analysis.
• Demonstrated experience in AI projects, including familiarity with AI and machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch, LLMs, GenAI).
• Proficiency in Python programming.
• Skills in anomaly detection, classification, clustering, optimization, and AI/AIOps and Knowledge in statistics, probability, and other mathematical concepts underlying them.
• Knowledge in AI model deployment and integration.
• Strong problem-solving capabilities, with the ability to adapt to a dynamic, fast-paced environment.
• Excellent communication and teamwork abilities


Job Responsibilities:


• Develop and maintain scalable data pipelines for various AI/ML-driven data services, ensuring reliable data ingestion, transformation, and integration from various sources, as well as maintaining ML feature and deployment pipelines.
• Ensure that data architectures and infrastructure can scale seamlessly as the data volume and complexity grow.
• Oversee ML design reviews, create best practices, and develop playbooks for end-to-end ML systems in production.
• Lead the development of UEBA, Anomaly detection, Threat detection etc., leveraging the available subject matter expertise and with independent research where required.
• Work with a great deal of autonomy and be the technical thought leader in creating a forward-looking vision with clear direction.
• Effectively communicate complex technical artifacts to both technical (engineers & scientists) and non-technical audiences.
• Collaborate closely with cross-functional teams, including business stakeholders, to innovate and unlock new use cases for our customers driven by data intelligence.


Preferred Technical and Professional Expertise:


• Advanced experience with technologies including Elasticsearch, Kafka, SIEM, SOAR, real-time data aggregation, and enrichment.
• Good understanding of enterprise cybersecurity landscape and security related data.
• Experience in design and technical leadership.

Skills from this JD

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

Machine Learning Primary No API 2 row (run stopped after API 1 or history missing)

Data Pipelines Primary No API 2 row (run stopped after API 1 or history missing)

Data Ingestion Primary No API 2 row (run stopped after API 1 or history missing)

Data Transformation Primary No API 2 row (run stopped after API 1 or history missing)

Data Integration Primary No API 2 row (run stopped after API 1 or history missing)

ML Pipelines Primary No API 2 row (run stopped after API 1 or history missing)

Feature Engineering Primary No API 2 row (run stopped after API 1 or history missing)

ML Systems Primary No API 2 row (run stopped after API 1 or history missing)

UEBA Primary No API 2 row (run stopped after API 1 or history missing)

Anomaly Detection Primary No API 2 row (run stopped after API 1 or history missing)

Threat Detection Primary No API 2 row (run stopped after API 1 or history missing)

Library artifacts (this run)

No artifact rows for this run.

nano JD Parser — gpt-4.1-nano click to toggle

RolePrincipal AI/ML Engineer

Experience6+ years

DomainCybersecurity

Location Hyderabad, India

JD type pass

Show raw JSON

{
  "JD_type": "pass",
  "about_company": null,
  "certifications": [],
  "company_name": null,
  "ctc": null,
  "domain": {
    "primary": {
      "aliases": [
        "IT Security",
        "Information Security"
      ],
      "domain": "Cybersecurity"
    },
    "secondary": null
  },
  "education": [],
  "experience": {
    "max": null,
    "min": 6,
    "raw": "6+ years"
  },
  "job_locations": [
    {
      "aliases": [
        "Hyderabad, TG"
      ],
      "city": "Hyderabad",
      "country": "India",
      "state": "Telangana",
      "work_mode": null
    }
  ],
  "role": "Principal AI/ML Engineer",
  "role_aliases": [
    "AI/ML Engineer",
    "Machine Learning Engineer",
    "Data Scientist"
  ],
  "role_archetype": "Data",
  "roles_and_responsibilities": [
    {
      "bullet_count": 7,
      "heading": "Job Responsibilities",
      "heading_was_present": true,
      "source_marker": {
        "first_5_words": "\u2022 Develop and maintain scalable",
        "last_5_words": "driven by data intelligence."
      },
      "text": "\u2022 Develop and maintain scalable data pipelines for various AI/ML-driven data services, ensuring reliable data ingestion, transformation, and integration from various sources, as well as maintaining ML feature and deployment pipelines.\n\u2022 Ensure that data architectures and infrastructure can scale seamlessly as the data volume and complexity grow.\n\u2022 Oversee ML design reviews, create best practices, and develop playbooks for end-to-end ML systems in production.\n\u2022 Lead the development of UEBA, Anomaly detection, Threat detection etc., leveraging the available subject matter expertise and with independent research where required.\n\u2022 Work with a great deal of autonomy and be the technical thought leader in creating a forward-looking vision with clear direction.\n\u2022 Effectively communicate complex technical artifacts to both technical (engineers \u0026 scientists) and non-technical audiences.\n\u2022 Collaborate closely with cross-functional teams, including business stakeholders, to innovate and unlock new use cases for our customers driven by data intelligence.",
      "word_count": 164
    }
  ],
  "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"
    },
    {
      "is_primary": true,
      "skill_name": "Data Ingestion"
    },
    {
      "is_primary": true,
      "skill_name": "Data Transformation"
    },
    {
      "is_primary": true,
      "skill_name": "Data Integration"
    },
    {
      "is_primary": true,
      "skill_name": "ML Pipelines"
    },
    {
      "is_primary": true,
      "skill_name": "Feature Engineering"
    },
    {
      "is_primary": true,
      "skill_name": "ML Systems"
    },
    {
      "is_primary": true,
      "skill_name": "UEBA"
    },
    {
      "is_primary": true,
      "skill_name": "Anomaly Detection"
    },
    {
      "is_primary": true,
      "skill_name": "Threat Detection"
    }
  ],
  "jd_role": {
    "display_name": "Principal AI/ML Engineer",
    "rationale": null,
    "role_aliases": [
      "AI/ML Engineer",
      "Machine Learning Engineer",
      "Data Scientist"
    ],
    "role_archetype": "Data",
    "slug": ""
  },
  "nano_parsed": {
    "JD_type": "pass",
    "about_company": null,
    "certifications": [],
    "company_name": null,
    "ctc": null,
    "domain": {
      "primary": {
        "aliases": [
          "IT Security",
          "Information Security"
        ],
        "domain": "Cybersecurity"
      },
      "secondary": null
    },
    "education": [],
    "experience": {
      "max": null,
      "min": 6,
      "raw": "6+ years"
    },
    "job_locations": [
      {
        "aliases": [
          "Hyderabad, TG"
        ],
        "city": "Hyderabad",
        "country": "India",
        "state": "Telangana",
        "work_mode": null
      }
    ],
    "role": "Principal AI/ML Engineer",
    "role_aliases": [
      "AI/ML Engineer",
      "Machine Learning Engineer",
      "Data Scientist"
    ],
    "role_archetype": "Data",
    "roles_and_responsibilities": [
      {
        "bullet_count": 7,
        "heading": "Job Responsibilities",
        "heading_was_present": true,
        "source_marker": {
          "first_5_words": "\u2022 Develop and maintain scalable",
          "last_5_words": "driven by data intelligence."
        },
        "text": "\u2022 Develop and maintain scalable data pipelines for various AI/ML-driven data services, ensuring reliable data ingestion, transformation, and integration from various sources, as well as maintaining ML feature and deployment pipelines.\n\u2022 Ensure that data architectures and infrastructure can scale seamlessly as the data volume and complexity grow.\n\u2022 Oversee ML design reviews, create best practices, and develop playbooks for end-to-end ML systems in production.\n\u2022 Lead the development of UEBA, Anomaly detection, Threat detection etc., leveraging the available subject matter expertise and with independent research where required.\n\u2022 Work with a great deal of autonomy and be the technical thought leader in creating a forward-looking vision with clear direction.\n\u2022 Effectively communicate complex technical artifacts to both technical (engineers \u0026 scientists) and non-technical audiences.\n\u2022 Collaborate closely with cross-functional teams, including business stakeholders, to innovate and unlock new use cases for our customers driven by data intelligence.",
        "word_count": 164
      }
    ],
    "urls": []
  },
  "rejected": false,
  "rejection_reason": null,
  "run_id": "b140db0a-27ea-4c0a-af6a-0542291a2e8a",
  "stage3_signals": {
    "alias_found": true,
    "alias_match_roles": [
      {
        "display_name": "ML Engineer",
        "matched_count": null,
        "role_id": 3,
        "score": 1.0,
        "slug": "ml-engineer",
        "total_count": null
      }
    ],
    "kra_match_roles": [
      {
        "display_name": "ML Engineer",
        "matched_count": null,
        "role_id": 3,
        "score": 0.4453,
        "slug": "ml-engineer",
        "total_count": null
      },
      {
        "display_name": "Data Engineer",
        "matched_count": null,
        "role_id": 2,
        "score": 0.3976,
        "slug": "data-engineer",
        "total_count": null
      },
      {
        "display_name": "AI Compliance Officer",
        "matched_count": null,
        "role_id": 12,
        "score": 0.3911,
        "slug": "ai-compliance-officer",
        "total_count": null
      },
      {
        "display_name": "DevOps Engineer",
        "matched_count": null,
        "role_id": 10,
        "score": 0.3824,
        "slug": "devops-engineer",
        "total_count": null
      },
      {
        "display_name": "AI Engineer",
        "matched_count": null,
        "role_id": 13,
        "score": 0.3654,
        "slug": "ai-engineer",
        "total_count": null
      }
    ],
    "skill_match_roles": [
      {
        "display_name": "ML Engineer",
        "matched_count": 2,
        "role_id": 3,
        "score": 0.1818,
        "slug": "ml-engineer",
        "total_count": 11
      },
      {
        "display_name": "ML Ops Engineer",
        "matched_count": 2,
        "role_id": 16,
        "score": 0.1818,
        "slug": "ml-ops-engineer",
        "total_count": 11
      },
      {
        "display_name": "Data Engineer",
        "matched_count": 1,
        "role_id": 2,
        "score": 0.0909,
        "slug": "data-engineer",
        "total_count": 11
      },
      {
        "display_name": "AI Engineer",
        "matched_count": 1,
        "role_id": 13,
        "score": 0.0909,
        "slug": "ai-engineer",
        "total_count": 11
      }
    ]
  },
  "stage4_decision": {
    "alias_collision_detected": true,
    "case": "D",
    "chosen_role": {
      "display_name": "ML Engineer",
      "matched_count": null,
      "role_id": 3,
      "score": 0.4453,
      "slug": "ml-engineer",
      "total_count": null
    },
    "confidence": 0.9,
    "llm2_fired": true,
    "llm2_reasoning": "The JD\u2019s emphasis on building scalable data/feature pipelines, productionizing ML systems, overseeing design reviews, and defining best practices aligns closely with the day-to-day responsibilities of an ML Engineer.",
    "queued": false,
    "reasoning": "LLM2 picked ml-engineer (confidence 0.90)"
  },
  "stage5_updates": {
    "centroid_n_after": 13,
    "centroid_updated": true,
    "collision_log_id": 65,
    "new_kra_attached": null,
    "new_skills_attached": [
      {
        "is_primary": true,
        "queue_id": 1407,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Data Pipelines",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1408,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Data Ingestion",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1409,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Data Transformation",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1410,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Data Integration",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1411,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "ML Pipelines",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1412,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Feature Engineering",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1413,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "UEBA",
        "status": "pending"
      },
      {
        "is_primary": true,
        "queue_id": 1414,
        "role_display_name": "ML Engineer",
        "role_slug": "ml-engineer",
        "skill_name": "Threat Detection",
        "status": "pending"
      }
    ],
    "queue_entry_id": null,
    "v3_pipeline_triggered": false,
    "v3_role_slug": null,
    "v3_run_id": null
  }
}

API 2 — extract-details

{}

API 3 — final-role-output

{}

LLM Calls

Every model call made for this run, in pipeline order. Click a card to see the model's response.

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