FULLTIME
Data Scientist ( Agentic AI / GenAI Engineer )_Rz
IAssess Consultants LLP
Not specified · onsite · Posted 8d ago
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Not disclosed6–8 yrsfulltimeNot specified
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Section · 01
About this role
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.
Skills: cloud run,generative ai,fast api,llm,llms,python,google cloud platform,ml,agentic ai,google cloud ai,ai/ml,data scientist,cloud,google cloud ai stack,gke,vertex ai,rag,vector search,gcp,google cloud,generative,ai
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: cloud run,generative ai,fast api,llm,llms,python,google cloud platform,ml,agentic ai,google cloud ai,ai/ml,data scientist,cloud,google cloud ai stack,gke,vertex ai,rag,vector search,gcp,google cloud,generative,ai
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Section · 02
Skills
Agent Orchestration using ADKRAG implementation with vector databasesAutoGenPrompt Engineeringvector searchCrewaigoogle cloud platformSemantic KernelFAISSWeaviateChromaDBpgvectorGoogle Cloud RunGoogle Vertex AIFlask FrameworkGoogle Kubernetes EngineLLM Application DevelopmentPythonPinecone DBLangchainLlamaindexFastAPI