AI Solutions Architect

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

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.

build small, high-signal datasets.

bias / fairness checks for ML.

Cost / perf optimization : model selection / routing, token budgeting, latency tuning, caching, semantic telemetry.

coordinate batch / real-time inference hooks with platform team.

Mentorship : guide 2–3 juniors on experiments, code quality, and research synthesis.

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.

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 .

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.

ship iteratively with evidence.

Nice to have

vector DBs (pgvector / FAISS / Pinecone / Weaviate).

PyTorch experience for small finetunes.

causal inference / upliftmodeling for experiments.

HRTech exposure (ATS / CRM, interview orchestration, assessments).