Field AI Engineer

At Bristol Myers Squibb, our employees often ask, “Who are you working for?”—a question that fuels collaboration, accountability, and urgency in our work. Our purpose-driven culture inspires us to discover, develop, and deliver innovative medicines to prevail over serious diseases. We offer uniquely interesting and meaningful work, opportunities for growth, and a supportive environment that values inclusion, wellbeing, flexibility, and comprehensive benefits. This is work that transforms the lives of patients, and the careers of those who do it.

Position Summary

We are seeking a curious, hands-on engineer to join our Global Sales Systems and Capabilities team within CBI&T (Commercial Business Insights & Technology) in Hyderabad. This role is focused on building AI-powered capabilities for our field force on Veeva Vault CRM – from LLM-based assistants and agentic workflows to the data pipelines and integrations that ground them in trusted commercial data. The ideal candidate has 2–4 years of experience in software or data engineering with Python, SQL and AWS, and at least 1 year of hands-on experience taking LLM/GenAI features from prototype to production. This is an applied AI engineering role – deep ML research or model-training experience is not required; what matters most is a builder with a product mindset who cares whether what they ship is used and trusted by field users. If you want an exciting and rewarding career that is meaningful, consider joining our diverse team!

Key Responsibilities

• Design, build and ship AI/LLM-powered features for Vault CRM field users – such as pre-call insights, call note summarization, conversational assistants and next-best-action recommendations – using prompt engineering, retrieval-augmented generation (RAG), tool/function calling and agentic workflows.
• Build and maintain the data foundation for AI: cloud-based (AWS) batch and event-driven pipelines (Glue, Lambda, API Gateway, EventBridge) and integrations with Vault CRM (e.g., Vault REST APIs, Direct Data API) and related commercial systems that supply clean, timely and governed context to AI applications.
• Implement retrieval components – document ingestion, chunking, embeddings, vector/hybrid search and metadata filtering – and tune them for relevance and accuracy.
• Integrate foundation models through enterprise platforms (e.g., Amazon Bedrock, Anthropic, Azure OpenAI) and expose AI capabilities through secure APIs, Model Context Protocol (MCP) servers and/or native Vault CRM extensions.
• Make evaluation part of the build: create test datasets, define qual...

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