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

Required

• 5–7 years of hands-on experience in data engineering and/or applied AI/ML roles.
• Strong proficiency in Python, Spark, and SQL for data pipeline development and data manipulation.
• Experience with AWS cloud services — S3, Glue, Lambda, or equivalent — for building cloud-native data workflows.
• Hands-on experience with dbt for data transformation, testing, and documentation.
• Experience with Databricks for data processing, model development, or both.
• Working knowledge of Medallion architecture patterns (bronze/silver/gold layering).
• Experience building and deploying ML models — including feature engineering, model training, evaluation, and integration into applications.
• Exposure to GenAI and LLM patterns — RAG, prompt engineering, conversational agents, or agentic AI workflows.
• Comfortable working with messy, real-world operational data from multiple source systems.
• Strong communication skills — you can explain data and AI concepts to non-technical stakeholders and collaborate effectively with engineers.
• A pragmatic, prototyping mindset — you optimize for speed and usefulness, not perfection.

Preferred / Nice-to-Have

• Experience in pharma, life sciences, or manufacturing environments.
• Familiarity with GPS/PDS domain systems — SAP, MES, LIMS, planning tools (Kinaxis, Blue Yonder, or similar).
• Experience with React.js or Node.js — enough to build lightweight data-backed interfaces or APIs when needed.
• Experience with agent-based AI patterns, decision intelligence, or multi-step AI reasoning workflows.
• Familiarity with data quality frameworks and data lineage concepts.
• Experience working in innovation labs, prototyping teams, or fast-paced delivery environments.

If you come across a role that intrigues you but doesn’t perfectly line up with your resume, we encourage you to apply anyway. You could be one step away from work that will transform your life and career.

Uniquely Interesting Work, Life-changing Careers
With a single vision as inspiring as “Transforming patients’ lives through science™ ”, every BMS employee plays an integral role in work that goes far beyond ordinary. Each of us is empowered to apply our individual talents and unique perspectives in a supportive culture, promoting global participation in clinical trials, while our shared values of passion, innovation, urgency, accountability, inclusion and integrity bring out the highest potential of each of our colleagues.

On-site Protocol

BMS has an occupancy structure that determines where an employee is required to conduct their work. This structure includes site-essential, site-by-design, field-based and remote-by-design jobs. The occupancy type that you are assigned is determined by the nature and responsibilities of your role:

Site-essential roles require 100% of shifts onsite at your assigned facility. Site-by-design roles may be eligible for a hybrid work model with at least 50% onsite at your assigned facility. For these roles, onsite presence is considered an essential job function and is critical to collaboration, innovation, productivity, and a positive Company culture. For field-based and remote-by-design roles the ability to physically travel to visit customers, patients or business partners and to attend meetings on behalf of BMS as directed is an essential job function.

Supporting People with Disabilities

BMS is dedicated to ensuring that people with disabilities can excel through a transparent recruitment process, reasonable workplace accommodations/adjustments and ongoing support in their roles. Applicants can request a reasonable workplace accommodation/adjustment prior to accepting a job offer. If you require reasonable accommodations/adjustments in completing this application, or in any part of the recruitment process, direct your inquiries to adastaffingsupport@bms.com. Visit careers.bms.com/eeo-accessibility to access our complete Equal Employment Opportunity statement.

Candidate Rights

BMS will consider for employment qualified applicants with arrest and conviction records, pursuant to applicable laws in your area.

If you live in or expect to work from Los Angeles County if hired for this position, please visit this page for important additional information: https://careers.bms.com/california-residents/

Data Protection

We will never request payments, financial information, or social security numbers during our application or recruitment process. Learn more about protecting yourself at https://careers.bms.com/fraud-protection.

Any data processed in connection with role applications will be treated in accordance with applicable data privacy policies and regulations.

If you believe that the job posting is missing information required by local law or incorrect in any way, please contact BMS at TAEnablement@bms.com. Please provide the Job Title and Requisition number so we can review. Communications related to your application should not be sent to this email and you will not receive a response. Inquiries related to the status of your application should be directed to Chat with Ripley.

R1602052 : AI & ML Engineer