AI & ML Engineer

Working with Us
Challenging. Meaningful. Life-changing. Those aren’t words that are usually associated with a job. But working at Bristol Myers Squibb is anything but usual. Here, uniquely interesting work happens every day, in every department. From optimizing a production line to the latest breakthroughs in cell therapy, this is work that transforms the lives of patients, and the careers of those who do it. You’ll get the chance to grow and thrive through opportunities uncommon in scale and scope, alongside high-achieving teams. Take your career farther than you thought possible.

Bristol Myers Squibb recognizes the importance of balance and flexibility in our work environment. We offer a wide variety of competitive benefits, services and programs that provide our employees with the resources to pursue their goals, both at work and in their personal lives. Read more: careers.bms.com/working-with-us.

Position Summary

The Data & AI Engineer builds the data foundation and AI capabilities that power the Digital Lab’s prototypes and applications. You’ll pull data from operational source systems, build focused data pipelines to make it usable, and develop AI/ML models and GenAI integrations that turn that data into intelligent, working solutions.

This role combines hands-on data engineering with applied AI. You’ll build pipelines that are clean and fast enough to support rapid prototyping — structured using Medallion-style layering and dbt where appropriate — and you’ll build and integrate AI models, LLM-based agents, and machine learning workflows that the team’s applications consume. You work closely with Software Engineers who build the front-end experiences and Systems Analysts who define the requirements.

Key Responsibilities

Build Data Pipelines for Prototyping

• Build focused data pipelines that pull from GPS source systems (SAP, MES, LIMS, planning tools, quality systems) and transform raw data into clean, usable datasets for prototypes and applications.
• Use Spark, Python, SQL, and AWS services (Glue, S3) to extract, transform, and load data efficiently.
• Apply Medallion architecture patterns (bronze → silver → gold) to organize data layers — keeping it practical and proportional to the prototyping context.
• Use dbt for modular, testable transformations where appropriate — build reusable models that the team can leverage across projects.
• Prepare curated datasets tailored for AI/ML feature engineering, application consumption, and analytical exploration.

Develop & Integrate AI/ML Solutions

• Build and deploy machine learning models — predictive, classification, anomaly detection, and optimization models that support GPS use cases.
• Develop and integrate GenAI and LLM-based capabilities — including conversational agents, retrieval-augmented generation (RAG), and agentic AI workflows.
• Perform feature engineering — design and build feature sets from complex operational data that improve model performance and explainability.
• Work with Databricks for model development, experimentation, and deployment.
• Ensure AI outputs are structured and accessible for consumption by the team’s web applications and user-facing tools.

Collaborate Across the Digital Lab

• Partner with Software Engineers to define data contracts, APIs, and output formats that applications need to consume.
• Work with Systems Analysts to understand business requirements and translate them into data and AI solution approaches.
• Engage with business stakeholders when needed to understand data sources, validate data quality, and confirm that outputs reflect operational reality.
• Coordinate with centralized IT / BI&T teams for access to source systems, data governance considerations, and handoff when solutions graduate to production scale.

Keep It Practical and Prototype-Ready

• Build pipelines and models that are robust enough to support working prototypes and demos — not over-engineered for enterprise scale.
• Apply sound practices — version control, testing, documentation — proportional to the maturity of each project.
• Know when a pipeline or model is good enough for prototyping and when it needs to be hardened or handed off for production-grade implementation.

Qualifications / Education

• Bachelor’s or Master’s degree in Computer Science, Data Science, Computer Engineering, Information Systems, or a related field.

Experience & Skills

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