Associate Director, Senior Engineer
Listed on 2026-02-16
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Software Development
AI Engineer, Machine Learning/ 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: ‑with‑us.
Position SummaryAs a Senior Engineer you will design, implement, and optimize enterprise‑grade GenAI applications for Bristol Myers Squibb (BMS). Work hands‑on across the AI application lifecycle—architecture, development, deployment—and collaborate with cross‑functional teams to deliver impactful solutions on the cloud.
Key Responsibilities- AI Solution Design & Architecture:
Develop scalable and robust architectures for GenAI applications using cloud platforms (AWS, Azure, GCP). - Hands‑On Development:
Contribute high‑quality code in Python, React, and related tools; integrate and invoke advanced models with enterprise data sources. - Agentic AI Implementation:
Build and orchestrate GenAI agents using frameworks such as Lang Chain, Lang Graph, or similar.
- Bachelor’s or Master’s in Computer Science, Engineering, or related field.
- 5+ years experience in software/AI engineering, with practical exposure to the AI/ML production lifecycle.
- Strong hands‑on skills in Python, React, and GenAI frameworks (Lang Chain, Lang Graph, etc.).
- Experience architecting and deploying applications on AWS, Azure, and/or GCP.
- Proven track record developing and deploying enterprise‑grade solutions.
- Excellent collaboration and communication skills.
- Experience in life sciences or regulated industries.
- Familiarity with additional programming languages (Node.js, Java).
- Understanding of security, compliance, and performance optimization in enterprise environments.
- Experience with ML ops, data pipelines, and vector databases.
- Collaboration:
Work closely with other technical teams, data scientists, and business stakeholders to gather requirements and deliver technical solutions. - Production Deployment:
Package, deploy, and monitor production‑ready AI applications; leverage cloud‑native practices including containers and CI/CD pipelines. - Software Engineering Best Practices:
Participate in code reviews, maintain documentation, and ensure high code quality and technical standards. - Optimization & Troubleshooting:
Continuously improve model performance and reliability; identify and resolve technical issues in deployed systems. - Mentorship:
Provide technical guidance and share best practices with junior engineers when required. - Innovation:
Stay current with rapidly evolving AI technologies, frameworks, and industry trends; recommend improvements as appropriate.
#LI‑Hybrid
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.
Compensation OverviewMadison - Giralda - NJ - US: $169,150 - $204,970
Princeton - NJ - US: $169,150 - $204,970
Seattle - WA: $186,070 - $225,467
The starting compensation range(s) for this role are listed above for a full‑time employee (FTE) basis. Additional incentive cash and stock opportunities (based on eligibility) may be available. The starting pay rate takes into account characteristics of the job, such as required skills, where the job is performed, the employee’s work schedule, job‑related knowledge, and experience. Final, individual compensation will be decided based on demonstrated experience.
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