Datazymes Analytics -Lead AI Engineer (Agentic Systems)

Naukri E-hire; Bengaluru; Hybrid; INR 1,500,000 - 2,100,000; Full time

Job summary

Naukri E-hire in Bengaluru, India seeks engineers to design and build agentic AI systems for pharma applications. You will own end-to-end subsystems, from topology to API contracts, and ensure regulatory alignment with GxP, 21 CFR Part 11, and HIPAA-like data handling.

You will lead RAG pipelines and observability initiatives in production environments. Ideal candidates have 5–7 years in software/ML engineering, hands-on with multiple agentic frameworks, Python expertise, and cloud deployment

Qualifications

• 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).
• Direct SDK experience: Claude/OpenAI/Vertex AI.
• Python mastery: production-quality code, tests, packaging, profiling.
• RAG pipeline depth: embedding models, vector stores, retrieval evaluation.
• Cloud deployment and CI/CD basics.

Responsibilities

• Design and implement complex components of agentic pipelines, multi-agent graphs, and memory systems.
• Take ownership of sub-systems: define topology, data flows, API contracts, failure handling.
• Build and optimise production RAG pipelines with ingestion, chunking, embeddings, and retrieval.
• Integrate agentic systems with pharma data platforms via REST and event-driven patterns.
• Own observability for components: tracing, cost metrics, drift alerts, evaluation harnesses.
• Lead CI/CD for owned modules: containerisation, tests, and rollback procedures.
• Translate medical affairs and analytics requirements into component specs with GxP considerations.
• Develop intelligent document processing pipelines for pharma content.
• Contribute to domain retrieval strategies and KOL mapping.

Skills

Production LLM/agentic AI

Agentic frameworks

Python

RAG pipelines

Cloud deployment

Docker/Kubernetes

Observability

CI/CD

pharma data handling

Tools

Pinecone

Weaviate

pgvector

Terraform

CDK

Docker

Kubernetes

Job description

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.

2. 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.

3. 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.

4. Good 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.