Career Category

Information Systems

Job Description

Job Description

ABOUT AMGEN

Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today.

ABOUT THE ROLE

What You Will Do

As a Principal Machine Learning Engineer, you will lead the architecture and development of a core AI platform capability that enables researchers and engineers across Amgen to build, deploy, and operate advanced ML and Generative AI systems at scale.

You will operate as the technical lead for a small engineering team and own the design and evolution of a platform that simplifies the lifecycle management of complex ML workloads including LLMs, fine-tuned SLMs, and next-generation AI systems.

This platform powers a self-service ML ecosystem that enables researchers to move from experimentation to production quickly, with built-in MLOps, observability, and governance capabilities.

In this role you will:

• Architect and build a scalable ML platform for training, deployment, and lifecycle management of ML, LLM, and Generative AI models
• Lead development of infrastructure that supports production hosting of complex AI systems, including large-scale inference workloads
• Design developer-friendly abstractions and automation that make it easy for researchers to build and deploy models within the Amgen ecosystem
• Implement and evolve MLOps capabilities including experiment tracking, model versioning, CI/CD for ML, monitoring, and reproducibility using tools such as Databricks and MLflow
• Build platform capabilities supporting Generative AI and emerging Agentic AI systems
• Serve as the technical leader for a team of engineers, guiding architecture, design reviews, and engineering best practices
• Partner with AI researchers, data scientists, and platform teams to translate cutting-edge AI research into reliable production systems
• Evaluate and adopt emerging technologies across the modern AI stack including foundation models, vector databases, agent frameworks, and model serving systems
• Champion AI-native engineering practices, leveraging tools like GitHub Copilot, Codex, and AI-assisted development workflows
• Contribute to the broader strategy and evolution of the Enterprise AI Platforms ecosystem

What We Expect of You

We are looking for a highly experienced engineer who combines deep ML systems expertise with strong technical leadership.

You should be comfortable operating at the intersection of machine learning, distributed systems, and developer platforms, while helping teams move quickly from research to production.

• Own the technical vision and delivery of a key AI platform capability
• Lead and mentor engineers while maintaining hands-on engineering contributions
• Build scalable, reliable ML infrastructure for training and inference workloads
• Enable self-service AI development for researchers and data scientists
• Stay current with modern AI technologies, including Generative AI and Agentic AI systems

Basic Qualifications

• Bachelor’s degree in computer science, Engineering, Data Science, or a related field with 12 to 17 years of total experience
• 8+ years of experience in software engineering, machine learning engineering, or ML infrastructure.
• Strong experience building production ML systems or ML platforms.
• Hands-on experience with MLOps frameworks and tools such as MLflow / Equivalent - Model lifecycle management frameworks
• Strong programming experience in Python and modern software engineering practices such as API Driven Architecture and Event based systems
• Experience designing scalable distributed systems or cloud-native architectures.
• Experience deploying and operating machine learning models in production environments.
• Solid understanding of modern ML workflows including training, evaluation, deployment, monitoring, and retraining.

Preferred Qualifications

• Advanced degree (Masters) in Computer Science, AI/ML, Data Science, or related discipline.
• Experience building infrastructure for LLMs, Generative AI, or foundation models
• Understanding of Agentic AI systems and orchestration frameworks
• Experience with LLM/SLM fine-tuning and production deployment
• Familiarity with modern AI ecosystem technologies such as:
• Retrieval-Augmented Generation (RAG)
• Vector databases
• Model serving frameworks
• Agent frameworks
• Experience building internal ML platforms used by researchers or data scientists
• Experience operating large-scale inference or GPU-based workloads

Soft Skills

• Strong technical leadership and mentoring ability
• Ability to drive architecture and technical direction
• Excellent cross-team collaboration and communication
• Strong ownership mindset and bias toward execution
• Passion for staying current with emerging AI technologies

EQUAL OPPORTUNITY STATEMENT

Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.

We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.

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