Gen AI Engineer :: 6+ Years experienced :: Multi-agent systems builder

Role: Gen AI Engineer

Experience: 5+ Years is a must

Notice Period: Immediate to 30 Days

About the Role

We're looking for a GenAI Engineer to build the agents themselves the prompts, tools, memory, retrieval and guardrails and tune them to real accuracy thresholds in production.

Location

Bengaluru, Chennai, Hyderabad, Kolkata, Mumbai, Pune, and Delhi-NCR (Gurugram)

or Coimbatore, Kochi, Bhubaneswar, Visakhapatnam, Indore

What You'll Do

• Build platform agents: Supervisor/Router, Retrieval, Policy Guard, Evidence & Audit, Notification, Human-Escalation.
• Build domain agents for the target process: Role Identifier, Approver Finder, Conflict/SoD Checker, Eligibility-Gate, Orchestrator, Deactivation/Leaver-Mover.
• Model Veeva Vault's access-control concepts security profiles, roles, groups, and Dynamic Access Control (DAC) into the knowledge graph so agents reason correctly about who should get access to what.
• Translate Veeva Vault integration and entitlement data (users, security profiles, DAC rules, product/domain access) into agent-ready context via retrieval and tool calls.
• Design and build the RAG pipeline: ingestion, chunking, embeddings, re-rank, citation and grounding.
• Build the knowledge graph (e.g. on neo4j) covering role/entity relationships and the vector-store schema.
• Own prompt architecture, the system-prompt library and its versioning; configure Bedrock guardrails.
• Seed golden datasets with subject-matter experts and run tuning cycles against evaluation findings.
• Optimise latency and cost model routing, caching, prompt compression.
• Build explainability for reviewers rationale, citations, confidence and drift instrumentation.

What You'll Own

• 12+ working agents
• Prompt library
• RAG pipeline
• Knowledge graph model
• Guardrail configuration
• Model cards & knowledge-base lineage
• Agent runbook and tuning playbook

What You'll Bring

• 4-7+ years building LLM/GenAI applications, including 1-2+ years building multi-agent systems in production.
• Hands-on experience with AWS Bedrock (or equivalent) and an agent-orchestration framework (AgentCore, LangGraph, or similar).
• Strong RAG engineering skills: chunking strategy, embeddings, re-ranking, grounding and citation.
• Experience modelling and querying knowledge graphs (neo4j preferred) and vector stores.
• Strong Python skills; prompt engineering and prompt-library/version-management discipline.
• Working knowledge of Veeva Vault's security/entitlement model security profiles, roles, groups, and Dynamic Access Control (DAC) setup and how to ground agent decisions in it.
• Veeva integration exposure (Vault REST API / VQL) for pulling user, role and access data into RAG/knowledge-graph pipelines is a strong plus.
• Experience running structured tuning cycles against evaluation feedback in partnership with subject-matter experts.
• Working knowledge of LLM guardrails, cost/latency optimisation (model routing, caching, prompt compression).
• Comfort explaining model behaviour (rationale, citations, confidence) to non-technical business reviewers.