Principal Machine Learning Engineer

We are seeking a Principal Machine Learning Engineer Amgens most senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI solutions. Sitting at the intersection of engineering excellence and data-science enablement, you will develop, deploy and monitor modelsclassical ML, deep learning and LLMssecurely and cost-effectively. Acting as a player-coach, you will establish AI solution strategy, define technical standards, and partner with DevOps, Security, Compliance and Product teams to deliver a frictionless, enterprise-grade AI solutions.

Roles & Responsibilities:

• 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.
• Build production ML/GenAI solutions and lightweight apps delivering sub-second insights.
• 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.
• Establish LLM observability , SLOs, and safe deploys (blue-green/canary, shadow, rollbacks) with incident runbooks.
• Implement rigorous LLM evaluation frameworks and tooling
• Architect LLM/RAG with prompt management, safety guardrails, and optimized inference.
• Enforce data quality , lineage, and model/data cards; apply privacy-preserving techniques where needed.
• Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness.
• Prototype and benchmark new algorithms , offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs.
• Translate domain needs (R&D, Manufacturing, Commercial) into roadmaps; mentor teams and communicate trade-offs.

Must-Have Skills:

• 3-5 years in AI/ML and enterprise software.
• Strong command of Agentic AI frameworks
• Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
• Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, LangGraph, Semantic Kernel).
• Proficiency in Python and Java; containerization (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines).
• Strong business-case skillsable to model TCO vs. NPV and present trade-offs to executives.
• Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.

Good-to-Have Skills:

• Experience in Biotechnology or pharma industry is a big plus
• Published thought-leadership or conference talks on enterprise GenAI adoption.
• Masters degree in Computer Science and or Data Science
• Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.

Education and Professional Certifications

• Masters degree with years of experience in Computer Science, IT or related fieldOR
• Bachelors degree with years of experience in Computer Science, IT or related field
• Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus.

Soft Skills:

• Excellent analytical and troubleshooting skills.
• Strong verbal and written communication skills
• Ability to work effectively with global, virtual teams
• High degree of initiative and self-motivation.
• Ability to manage multiple priorities successfully.
• Team-oriented, with a focus on achieving team goals.
• Ability to learn quickly, be organized and detail oriented.
• Strong presentation and public speaking skills.