Experience Required

- 6+ years in AI/ML, data science, or software engineering

- 3+ years in GenAI, LLM, RAG, conversational AI, or ML productionisation

Key Responsibilities

- Build and deploy secure, production‑grade AI agents using GenAI frameworks

- Develop APIs with Python (FastAPI, Flask, or similar)

- Implement RAG architecture: ingestion, chunking, embeddings, retrieval, ranking, grounding, citations, evaluation

- Apply LLM techniques: embeddings, vector search, prompt design, evaluation, hallucination control

- Manage MLOps/LLMOps: deployment, monitoring, evaluation, versioning, observability

- Ensure security and governance: IAM, PII handling, prompt injection risks, data leakage prevention, approval workflows

- Deliver working prototypes and production‑ready services

Must‑Have Skills

- Python, FastAPI, REST APIs, async processing

- LLM application development and RAG implementation with vector databases





- Prompt engineering, tool/function calling, structured outputs

- LLM security: prompt injection, guardrails, access control

- GCP Vertex AI, Gemini, Cloud Run, GKE, or equivalent

- Agent orchestration using ADK, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar

- Evaluation frameworks: Vertex AI, Evals, RAGAS, regression tests

Nice to Have (Trainable)

- Google Agent Development Kit (ADK)

- Gemini Enterprise Agent Platform

- Agent observability and tracing

- Multi‑agent architecture

- Human‑in‑the‑loop approval flows

- Enterprise knowledge graph / search integration

Short JD Data Scientists / GenAI Engineers with solid Python, LLM/RAG expertise, and production deployment experience.

📌 Agentic AI / GenAI Engineer - JG (India)
🏢 IAssess Consultants
📍 India