Principal Machine Learning Engineer
Core
Own enterprise AI/ML architecture, standards, and guardrails while building and scaling end-to-end machine-learning and generative-AI solutions for Amgen's R&D, Manufacturing, and Commercial domains.
Role type
Principal Machine Learning Engineer (Enterprise AI/ML Architect & Player-Coach)
Builds
Production ML/GenAI solutions, lightweight apps, full-stack AI applications, and reusable ML components (feature stores, model registries).
Domain
Biotechnology / Pharma / Enterprise AI
Deliverable
production ML models | product features | infrastructure
Required skills
Machine learning algorithms (regression, ensembles, deep learning, LLMs/RAG), MLOps (Kubeflow, SageMaker Pipelines), Python, Java, Cloud platforms (AWS, Azure, GCP), Containerization (Docker/K8s), Data quality & lineage management, Stakeholder management, Business case modeling (TCO vs NPV), Team leadership.
Preferred skills
Biotechnology/pharma industry experience, Published thought leadership on GenAI, Master's in CS/Data Science, Agile/SAFe methodologies, GenAI/ML platform certifications.
Technologies
Kubeflow, SageMaker Pipelines, Open AI SDK, LangChain, LangGraph, Semantic Kernel, Docker, K8s, AWS, Azure, GCP, GitHub Actions, Bedrock.
Responsibilities
Own enterprise AI/ML architecture and standards; Build end-to-end ML pipelines and production GenAI solutions; Establish observability, SLOs, and safe deployment strategies; Lead rigorous model evaluation, drift detection, and automated retraining; Architect LLM/RAG systems with safety guardrails; Contribute reusable ML components and evangelize best practices; Translate domain needs into roadmaps and mentor teams.
Seniority
Principal, hands-on IC with leadership responsibilities