Lead Applied AI Engineer

2 hours ago

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

Lead Applied AI Engineer

Location:
Gurgaon

Function:
Engineering (Applied AI)

Reports to:
CTO

Team:
2-3 AI engineers

Experience:
8–10 years (majority in Applied AI/LLMs; solid traditional ML)

Why this role

We're building agentic AI for recruitment workflows—sourcing, screening, interview assistance, and offer orchestration. You'll own LLM/agent design, retrieval, evaluation, safety, and targeted
traditional ML
models where they outperform or complement LLMs.

What you'll do

  • Hands-on AI (70–80%):
    design & build
    agent workflows
    (tool use, planning/looping, memory, self-critique) using
    multi-agent frameworks
    (e.g.,
    LangChain
    ,
    LangGraph
    ; plus experience with similar ecosystems like AutoGen/CrewAI is a plus).
  • Retrieval & context (RAG):
    chunking, metadata, hybrid search, query rewriting, reranking, and context compression.
  • Traditional ML:
    design and ship supervised/unsupervised models for ranking, matching, dedup, scoring, and risk/quality signals.
  • Feature engineering, leakage control, CV strategy, imbalanced learning, and calibration.
  • Model families: Logistic/Linear, Tree ensembles, kNN, SVMs, clustering, basic time-series.
  • Evaluation & quality:
    offline/online evals (goldens, rubrics, A/B), statistical testing, human-in-the-loop; build small, high-signal datasets.
  • Safety & governance:
    guardrails (policy/PII/toxicity), prompt hardening, hallucination containment; bias/fairness checks for ML.
  • Cost/perf optimization:
    model selection/routing, token budgeting, latency tuning, caching, semantic telemetry.
  • Light MLOps (in-collab):
    experiment tracking, model registry, reproducible training; coordinate batch/real-time inference hooks with platform team.
  • Mentorship:
    guide 2–3 juniors on experiments, code quality, and research synthesis.
  • Collaboration:
    pair with full-stack/infra teams for APIs/deploy; you won't own K8s/IaC.

What you've done (must-haves)

  • 8–10 years in software/AI with recent deep focus on
    LLMs/agentic systems
    plus delivered
    traditional ML
    projects.
  • Strong
    Python
    ; solid stats/ML fundamentals (bias-variance, CV, A/B testing, power, drift).
  • Built
    multi-agent
    or tool-using systems with
    LangChain
    and/or
    LangGraph
    (or equivalent), including function/tool calling and planner/executor patterns.
  • Delivered
    RAG
    end-to-end with
    vector databases
    (
    pgvector/FAISS/Pinecone/Weaviate
    ), hybrid retrieval, and cross-encoder
    re-ranking
    .
  • Trained and evaluated production ML models using
    scikit-learn
    and tree ensembles (
    XGBoost/LightGBM/CatBoost
    ); tuned via grid/Bayes/Optuna.
  • Set up
    LLM and ML evals
    (RAGAS/DeepEval/OpenAI Evals or custom), with clear task metrics and online experiments.
  • Implemented
    guardrails & safety
    and measurable quality gates for both LLM and ML features.
  • Product sense: translate use-cases into tasks/metrics; ship iteratively with evidence.

Nice to have

  • Re-ranking (bi-encoders/cross-encoders), ColBERT; semantic caching; vector DBs (pgvector/FAISS/Pinecone/Weaviate).
  • Light model serving (vLLM/TGI) and adapters (LoRA); PyTorch experience for small finetunes.
  • Workflow engines (Temporal/Prefect); basic time-series forecasting; causal inference/uplift modeling for experiments.
  • HRTech exposure (ATS/CRM, interview orchestration, assessments).

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