Project Leader

Job ResponsibilitiesAI Developer Commercial Pharma Analytics

Location: [India] Reports to: MDM Data Governance CoE Lead / Practice Lead Team: AI/Agentic Engineering, Commercial Data Solutions

Role Summary

We''re looking for an AI Developer to build and productionize agentic AI, LLM-based, and applied ML solutions that power commercial data platforms (MDM, data governance, HCP/HCO mastering, customer 360) for life sciences clients. This is a hands-on engineering role you''ll design, code, evaluate, and deploy AI systems, not just prototype them. Pharma domain knowledge is a plus, not a prerequisite; we''ll teach you the industry context. What matters is that you can ship reliable AI products.

What You''ll Do

• Design and build LLM-powered agents (RAG pipelines, tool-using agents, multi-agent orchestration) for use cases like entity resolution, data stewardship automation, hierarchy/relationship inference, and natural-language querying over master data
• Develop and fine-tune models (prompt engineering, fine-tuning, embeddings, evaluation harnesses) and integrate them into production pipelines
• Build robust evaluation frameworks accuracy, hallucination rate, latency, cost and iterate against them
• Own the full lifecycle: architecture, coding, testing, deployment, monitoring, and versioning of AI components
• Integrate AI services with enterprise data platforms (Informatica IDMC, Reltio, Databricks, Snowflake, etc.) via APIs and MCP-style tool connectors
• Collaborate with solution architects and delivery leads to translate client requirements (e.g., HCP affiliation logic, consent management, account hierarchy) into AI-driven technical designs
• Build internal accelerators/reusable agent frameworks that can be deployed across multiple client engagements
• Contribute to technical proposals and PoCs during pre-sales when needed

Must-Have Technical Skills

• Strong Python engineering skills (not just notebooks production code, testing, packaging)
• Hands-on experience with LLM APIs (OpenAI, Anthropic, etc.) and orchestration frameworks (LangChain, LangGraph, LlamaIndex, or equivalent)
• Practical experience building RAG systems: chunking strategies, vector databases (Pinecone, Weaviate, pgvector, etc.), retrieval evaluation
• Understanding of agentic architectures: tool calling, function calling, multi-step planning, memory/state management
• Experience with prompt engineering and systematic prompt evaluation (not trial-and-error)
• Familiarity with model fine-tuning and/or embedding model selection/tuning
• API development (REST, sometimes MCP) and integration with third-party/enterprise systems
• Comfortable with cloud platforms (Azure/AWS/GCP) and basic MLOps (CI/CD for models, monitoring, logging)
• SQL and experience working with structured/semi-structured enterprise data

Nice-to-Have

• Exposure to master data management concepts (match/merge, golden records, survivorship) will be trained if not present
• Experience with Databricks, Snowflake, or similar data platforms
• Familiarity with life sciences/pharma commercial data (HCP/HCO, territory, consent) helpful but not required
• Experience building internal tools/accelerators reused across projects
• Contributions to open-source AI tooling

EducationBE/B.TechMaster of Computer ApplicationBehavioural CompetenciesOwnershipTeamwork LeadershipCultural FitMotivation to Learn and GrowTechnical CompetenciesProblem SolvingLifescience KnowledgeCommunicationCapability Building / Thought LeadershipSkills