Note: The job is a remote job and is open to candidates in USA. Amgen is a biotechnology company focused on developing medicines for serious illnesses. The Principal Machine Learning Engineer will serve as the senior individual-contributor authority for enterprise AI/ML architecture and will build, deploy, monitor, and scale secure machine-learning and generative-AI solutions. The role also involves establishing technical standards, partnering with cross-functional teams, mentoring teams, and translating business needs into AI roadmaps.
Responsibilities
- Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem
- Build production ML/GenAI solutions and lightweight apps delivering sub-second insights
- Build end-to-end ML pipelines—data ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotion—using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks
- Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency
- Establish observability, SLOs, and safe deploys (blue-green/canary, shadow, rollbacks) with incident runbooks
- Lead rigorous evaluation (offline/online, A/B), drift detection, and automated retraining
- 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
- Contribute reusable ML/GenAI components—feature stores, model registries, experiment-tracking libraries—and evangelize best practices that raise engineering velocity across squads
- 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
Skills
- Doctorate degree and 2 years of Machine Learning Engineer experience
- Master's degree and 6 years of Machine Learning Engineer experience
- Bachelor's degree and 8 years of Machine Learning Engineer experience
- Associate's degree and 10 years of Machine Learning Engineer experience
- High school diploma / GED and 12 years of Machine Learning Engineer experience
- In addition to meeting at least one of the above requirements, you must have a minimum of 2 years experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation or resources. Your managerial experience may run concurrently with the required technical experience referenced above
- 3-5 years in AI/ML and enterprise software
- Strong command of machine-learning algorithms—regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques—with the judgment to choose, tune and operationalize the right method for a given business problem
- 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 skills—able to model TCO vs. NPV and present trade-offs to executives
- Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives
- Master's degree with 10-12 + years of experience in Computer Science, IT or related field
- Bachelor's degree with 12-14 + years of experience in Computer Science, IT or related field
- 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
- Experience in Biotechnology or pharma industry is a big plus
- Published thought-leadership or conference talks on enterprise GenAI adoption
- Master's degree in Computer Science and or Data Science
- Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery
- Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus
Benefits
- A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
- A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
- Stock-based long-term incentives
- Award-winning time-off plans
- Flexible work models where possible
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