Agentic AI

4 days ago

Gurgaon, Haryana, India Taggd Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Why this role

We're building enterprise‑grade
Agentic AI platform & applications for recruitment
—from sourcing and screening to interview assistance and offer orchestration. You'll lead a small, high‑leverage team that ships fast, measures rigorously, and scales responsibly.

What you'll do

  • Own delivery end‑to‑end:
    backlog, execution, quality, and timelines for Agentic AI features.
  • Be hands‑on (30–50% coding):
    set the technical bar in Python/TypeScript; review PRs; unblock tricky problems.
  • Design agentic systems:
    tool‑use orchestration, planning/looping, memory, safety rails, and cost/perf optimization.
  • Leverage LLMs smartly:
    RAG, structured output, function/tool calling, multi‑model routing; evaluate build vs. buy.
  • Ship production ML/LLM workflows:
    data pipelines, feature stores, vector indexes, retrievers, model registries.
  • MLOps & Observability:
    automate training/inference CI/CD; monitor quality, drift, toxicity, latency, cost, and usage.
  • EVALs & quality:
    define task‑level metrics; set up offline/online EVALs (goldens, rubrics, human‑in‑the‑loop) and guardrails.
  • DevOps (T‑shaped):
    own pragmatic infra with the team—GitHub Actions, containers, IaC, basic K8s; keep prod healthy.
  • Security & compliance:
    enforce data privacy, tenancy isolation, PII handling; partner with Security for audits.
  • People leadership:
    recruit, coach, and grow a high‑trust team; establish rituals (standups, planning, postmortems).
  • Stakeholder management:
    partner with Product/Design/Recruitment SMEs; translate business goals into roadmaps.

What you've done (must‑haves)

  • 10+ years in software/ML; 4+ years leading engineers (TL/EM) in high‑velocity product teams.
  • Built and operated
    LLM‑powered
    or ML products at scale (user‑facing or enterprise workflows).
  • Strong coding in
    Python, Java
    and
    TypeScript/Node
    ; solid system design and API fundamentals.
  • Exposure to
    frontend
    technologies like React, Angular, Flutter
  • Experience on SQL databases like Postgres, MariaDB
  • Practical
    MLOps
    : experiment tracking, model registries, reproducible training, feature/vectors, A/B rollouts.
  • LLM tooling
    : orchestration (LangChain/LlamaIndex/DSPy), vector DBs (pgvector/FAISS/Pinecone/Weaviate), RAG patterns, context engineering
  • Observability & EVALs
    : ML/LLM monitoring, LLM eval frameworks (RAGAS/DeepEval/OpenAI Evals), offline+online testing and human review.
  • Comfortable with
    DevOps
    : GitHub Actions, Docker, basic Kubernetes, IaC (Terraform), and one major cloud (GCP/AWS/Azure).
  • Familiar with
    AI SDLC tools
    : GitHub Copilot, Cursor, Claude Code, Code Llama/Codex‑style tools; test automation.
  • Product mindset: measure outcomes (quality, cost, speed), not just outputs; data‑driven decisions.

Nice to have

  • HRTech/recruitment domain (ATS/CRM, assessments, interview orchestration).
  • Retrieval quality tuning, prompt‑engineering at scale, policy/guardrail systems (OpenAI/Guardrails/NeMo Guardrails).
  • Knowledge of
    multi‑agent
    frameworks, graph planners, or workflow engines (Prefect/Temporal).
  • Experience with
    privacy‑preserving ML
    , tenancy isolation, regionalization.

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