Data Scientist

Role : Data Scientist-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.