Lead Applied AI Engineer

Why this role

traditional ML

What you'll do

• Hands-on AI (7080%):

  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 23 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)

• 810 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).