Project Leader

Job Responsibilities AI 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

• Education BE/B.Tech Master of Computer Application Behavioural Competencies Ownership Teamwork & Leadership Cultural Fit Motivation to Learn and Grow Technical Competencies Problem Solving Lifescience Knowledge Communication Capability Building / Thought Leadership Skills