WHAT WILL YOU DO: 1. Architecture & Engineering • Design and implement complex components of agentic pipelines multi-agent graphs, tool orchestration layers, retrieval modules, and memory systems — using LangGraph, AutoGen, CrewAI, or equivalent.

- Take ownership of full sub-system designs: define agent topology, data flows, API contracts, and failure handling for a bounded scope, escalating trade-offs to the Sr. Architect.
- Build and optimise production RAG pipelines: document ingestion, chunking strategy, embedding selection, hybrid search, retrieval evaluation, and latency tuning.
- Integrate agentic systems with pharma data platforms (IQVIA, Symphony, Komodo, Veeva) via REST, event-driven hooks, and batch pipeline patterns.
- Own observability for your components: instrument trace logging, cost metrics, drift alerts, and evaluation harnesses using LangSmith, Helicone, or equivalent.
- Lead CI/CD for owned modules: containerisation (Docker/Kubernetes), automated test suites, staging gate criteria, and rollback procedures. 2.

Pharma Domain

Application • Translate medical affairs, commercial analytics, and clinical ops requirements into agent component specifications with minimal supervision.
- Apply 21 CFR Part 11 auditability, HIPAA-compatible data handling, and GxP traceability patterns to every component you build.
- Build intelligent document processing pipelines for pharma content: drug labels, clinical study reports, HEOR dossiers, and regulatory submissions.
- Contribute to KOL mapping, competitive intelligence, and signal detection agents with domain-aware retrieval and reasoning strategies 3.

Technical





Leadership & Mentorship • Serve as the day-to-day technical reference for AI Engineers on your pod: code review, design feedback, unblocking implementation issues.
- Lead component-level design reviews; surface architecture risks to the Sr. Architect or Associate Director before they reach staging.
- Pair with junior engineers on hard problems; document patterns and decisions in the team’s shared knowledge base.
- Represent engineering quality in client-facing technical discussions; translate complex trade-offs into plain language.
- Contribute reference implementations and guardrail templates to the firm’s internal agentic AI playbook WHAT YOU BRING 1. Technical depth (Required) • 5–7 years in software or ML engineering; 2+ years building and shipping production LLM or agentic AI systems.
- Hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI); you have debugged framework internals, not just followed tutorials.
- Direct SDK experience: Anthropic Claude API (tool use, streaming), OpenAI Assistants API, or Vertex AI Agent Builder.
- Python mastery: production-quality code, type annotations, unit and integration tests, packaging, and performance profiling.
- RAG pipeline depth: embedding model selection, vector stores (Pinecone,



Weaviate, pgvector), hybrid retrieval, RAGAS or custom evaluation harnesses.
- Cloud deployment: AWS, Azure, or GCP. Docker, Kubernetes, IaC basics (Terraform or CDK), CI/CD pipelines.
- Agent observability: LangSmith, Helicone, or equivalent — you have diagnosed latency, cost, and quality issues in production traces.
- Pharma / Life Sciences Domain (Required) • Working knowledge of pharma commercial data: Rx/claims, NPI-level analytics, brand performance metrics.
- Experience operating in regulated data environments: GxP, 21 CFR Part 11, HIPAA compliant data handling.
- Exposure to at least one of: medical affairs analytics, RWE, clinical operations data, HEOR/market access, or regulatory intelligence.
- Leadership & Communication (Required) • Track record of shipping 2+ agentic or ML systems to production — not just proof-of-concepts — with documented performance benchmarks.
- Experience acting as technical lead or senior reviewer for at least one junior engineer or cross functional delivery workstream.
- Ability to write crisp component specifications and communicate architectural trade-offs to both engineers and non-technical stakeholders.
- Valuable to Have • MCP (Model Context Protocol) implementation experience.
- Experience with Veeva Vault, Medidata, IQVIA, or Symphony Health platform integrations.
- Familiarity with knowledge graphs (Neo4j, Amazon Neptune) for pharma entity modelling.
- Exposure to RLHF, fine-tuning, or model adaptation workflows.
- Prior consulting or services-firm delivery experience

📌 Datazymes Analytics -Lead AI Engineer (Agentic Systems) (Bengaluru)
🏢 Naukri e-Hire
📍 Bengaluru