Associate Director, Machine Learning Engineer

Career Category

Information Systems

Job Description

Join Amgen’s Mission of Serving Patients

At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do.

Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.

Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.

Machine Learning Engineer Associate Director

What you will do

Let’s do this. Let’s change the world. In this vital role you will lead the design, development, and deployment of enterprise-scale agents and conversational bots. You will own the end-to-end architecture—from orchestration and retrieval to safety, observability, and CI/CD—so our agents reliably solve real business problems across R&D, Manufacturing, and Commercial. As a senior IC “player-coach,” you’ll set technical standards, mentor teams, and partner with Security, Compliance, DevOps, and Product to ship secure, resilient, and cost-efficient solutions with measurable impact.

Roles & Responsibilities:

• Own the enterprise agent/bot platform architecture (cloud/on-prem), API contracts, and guardrails for multi-tenant use.

• Design and ship production agentic systems: multi-agent planning, tool/function calling, workflow orchestration, and structured outputs.

• Build high-containment bots for web/mobile/Slack/Teams/IVR with streaming responses and sub-second turn latency where required.

• Implement retrieval & memory (RAG, vector stores, knowledge graphs, session memory) with data contracts, lineage, and lifecycle governance.

• Establish bot CI/CD: prompt & config versioning, replay/conversation tests, feature flags, and automated rollbacks. Implement progressive delivery (blue-green, canary) with health checks, feature flags, and one-click rollback.

• Stand up observability: tracing, metrics, and conversation analytics (containment, escalation, hallucination, CSAT, latency, cost per turn) with alerts and runbooks.

• Integrate securely with enterprise systems (e.g., SAP, Salesforce, ServiceNow, MES/LIMS/ELN/QMS) via robust connectors and least-privilege tool access.

• Embed safety, privacy, and compliance (content filtering, red-teaming, audit trails, secrets mgmt) aligned to GxP/21 CFR Part 11 and HIPAA/PII needs.

• Optimize inference and cost (routing, caching, batching, distillation/quantization) and drive GPU/accelerator/FinOps strategy.

• Evaluate and document trade-offs for models (open vs. proprietary), hosting options, and agent frameworks (e.g., LangChain, Semantic Kernel, Assistants APIs).

• Translate domain needs (R&D, Manufacturing, Commercial) into prioritized roadmaps; mentor teams and communicate technical trade-offs to stakeholders.

What we expect of you

We are all different, yet we all use our unique contributions to serve patients. The professional we seek will have these qualifications.

Basic Qualifications:

Doctorate degree and 2 years of Computer Science, IT or related field experience

Or

Master’s degree and 4 years of Computer Science, IT or related field experience

Or

Bachelor’s degree and 6 years of Computer Science, IT or related field experience

Or

Associate’s degree and 10 years of Computer Science, IT or related field experience

Or

High school diploma / GED and 12 years of Computer Science, IT or related field experience

Preferred Qualifications:

• 3-5 years in AI/ML and enterprise software.

• Knowledge graphs or structured reasoning (SPARQL, RDF/OWL) and tool-use planning with constraints.

• Safety tooling (jailbreak detection, content moderation, PII redaction) and automated red-team pip