**HOW MIGHT YOU DEFY IMAGINATION?** If you feel like you’re part of something bigger, it’s because you are. At Amgen, our shared mission—to serve patients—drives all that we do. It is key to our becoming one of the world’s leading biotechnology companies. We are global collaborators who achieve together—researching, manufacturing, and delivering ever-better products that reach over 10 million patients worldwide. It’s time for a career you can be proud of. **Live | What you will do** + Serve as the internal MLOps and technical enablement partner for data science teams across TA Delivery, Measurement, and Investment Analytics. + Design, build, and maintain reusable components to support modeling, experimentation, and insight delivery at scale. + Implement and maintain ML pipelines in **Databricks** , including model registration, versioning, and production deployment via **MLflow** . + Support advanced DS use cases such as RAG pipelines, vector DB integrations, and document-based inference with well-documented, secure code. + Build lightweight, user-friendly interfaces (e.g., **Streamlit** ) for visualization or prototype delivery across analytic teams. + Collaborate with Amgen’s enterprise engineering, architecture, and cloud ops teams to follow best practices in **AWS** , **S3** , and scalable compute. + Maintain clear documentation, version control, code reuse templates, and testing workflows to support robust and reusable deployment patterns. + Monitor deployed models for performance drift and retraining needs; establish visibility around pipeline health and telemetry. + Advise on connector usage, integration patterns, and technology decisions across CD&A analytics teams. **Thrive | What you can expect** As we work to develop treatments that take care of others, we also work to care for our teammates’ professional and personal growth and well-being. **Basic Qualifications** + Bachelor’s or Master’s in Computer Science, Software Engineering, Data Engineering, or related field. + 7–14 years of experience in MLOps, data engineering, or ML/AI product development. + Hands-on expertise in **Databricks** , **MLflow** , **Python** , **SQL** , and **AWS** (esp. S3, Lambda, EC2). + Experience deploying and monitoring ML models in production. + Understanding of DS workflows and comfort collaborating closely with modeling teams. **Preferred Qualifications** + Strong software engineering foundation: clean code, CI/CD, Git, containerization. + Familiarity with **RAG** , **vector databases** , **embedding pipelines** , and LangChain-like frameworks. + Experience with low-code interface tools like **Streamlit** or **Dash** . + Ability to coach analysts and DS teams on technical patterns, integrations, and deployment best practices. + Strong documentation habits and interest in scaling DS enablement as a capability.