Data Scientist ( AI / ML with GCP)

Compensation: ₹35L – ₹50L

Role : Agentic AI / GenAI Engineer Experience : 6+ years in AI/ML/data science/software engineering, with 3 years in GenAI, LLM, RAG, conversational AI, or ML productionisation.

Common Job Description :

Strong Python. API development using FastAPI, Flask, or similar. Understanding of LLMs, embeddings, vector search, prompt design, evaluation, and hallucination control. RAG architecture: ingestion, chunking, embeddings, retrieval, ranking, grounding, citations, evaluation. MLOps / LLMOps basics: model deployment, monitoring, evaluation, versioning, observability. Security and governance basics: IAM, PII handling, prompt injection risks, data leakage, approval workflows. Ability to build real working prototypes and production-ready services.

Short JD : Agentic AI / GenAI Engineers who can design and deploy secure, production-grade AI agents using Google Cloud AI stack or equivalent GenAI frameworks.

Notes : GenAI/Python/RAG profiles MUST and grooming possible on ADK/Vertex/Gemini Enterprise Alternatively, can try for : Python backend engineers with solid LLM/RAG project experience. ML engineers with Vertex AI and production deployment experience. Strong LangChain/LlamaIndex engineers who can ramp up on ADK.

Detailed JD :

Generic Skills (Must Have) Python, FastAPI, REST APIs, async processing. LLM application development RAG implementation with vector databases Prompt engineering, tool calling, function calling, structured outputs. LLM security: prompt injection, data leakage, access control, guardrails.

GCP Skills (Must Have) VertexAI : Alternative vector databases: Vector Search, Pinecone, Weaviate, FAISS, Chroma, pgvector, or equivalent. gemini Agent Orchestration using ADK: Alternatives: LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or equivalent. Cloud Run: Production deployment on Cloud Run, GKE, or equivalent. Evaluation using Vertex AI : Alternatives: Evals, RAGAS, custom eval frameworks, golden datasets, regression tests.

Nice to have (Trainable) Google Agent Development Kit. Agent Engine / Gemini Enterprise Agent Platform. Model Armor. Agent observability and tracing. Multi-agent architecture. Human-in-the-loop approval flows. Enterprise knowledge graph / search integration.