Data Scientist- PL

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.

Skills: rag,python,flask,data scientist,agentic ai,fast api,genai,gcp,google adk,llm,google cloud platform,vertex ai