Principal Machine Learning Engineer

Principal Machine Learning Engineer

Role Name: Principal Machine Learning Engineer

Department Name: AI Data Science

Role GCF: 6A

ABOUT THE ROLE

Role Description:

We are seeking a Principal Machine Learning EngineerAmgens most senior individual-contributor leaderto build and scale end-to-end machine-learning and generative-AI solutions for enterprise use cases. Sitting at the intersection of engineering excellence, platform enablement and business impact, you will develop, deploy and monitor modelsclassical ML, deep learning and LLM-based solutionssecurely, responsibly and cost-effectively. Acting as a player-coach, you will define technical standards, shape AI solution strategy, establish reusable patterns and reference architectures, and partner with DevOps, Security, Compliance, Product and business teams to deliver enterprise-grade AI solutions in a regulated environment.

• Own enterprise AI/ML architecture, engineering standards, APIs, guardrails and reusable patterns across cloud and on-prem environments.
• Build production ML/GenAI solutions and lightweight applications that deliver business-ready insights with performance, reliability and usability in mind.
• Build end-to-end ML pipelinesdata ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotionusing 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 and operational excellence through SLOs, safe deployment patterns (blue-green/canary, shadow, rollbacks), incident runbooks and production monitoring.
• Lead rigorous evaluation (offline/online, A/B), drift detection, and automated retraining.
• Architect LLM/RAG solutions with prompt management, grounding strategies, safety guardrails, evaluation frameworks and optimized inference patterns.
• Enforce data quality, lineage and responsible AI practices by maintaining model/data cards, privacy controls, traceability, auditability and human oversight where required.
• Contribute reusable ML/GenAI platform componentsfeature stores, model registries, experiment-tracking libraries, evaluation assets and deployment templatesand evangelize best practices that raise engineering velocity across teams.
• 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 across RD, Manufacturing and Commercial functions into technical roadmaps; influence solution design, mentor engineers and data scientists, and communicate trade-offs clearly to senior stakeholders.

• Lead architecture reviews and technical governance for enterprise AI initiatives, making sound build-vs-buy decisions and ensuring alignment with security, compliance and platform standards.

• Drive adoption in regulated environments by embedding validation readiness, explainability, risk controls and scalable operating practices into AI solutions from design through production.

Must-Have Skills:

• Overall career span of 10-12+ years building production-grade technology solutions with 3-5 years of hands-on experience 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 techniqueswith 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 ability to evaluate technical and business trade-offs, including cost, scalability, risk, ROI and time-to-value, and present recommendations to senior stakeholders.
• Exceptional stakeholder management and technical leadership; able to translate complex concepts into concise, outcome-oriented narratives and influence decisions across engineering, product and business teams.

Good-to-Have Skills:

• Experience in Biotechnology or pharma industry is a big plus
• Exposure to Value and Access, market access, payer, commercial analytics or related healthcare business domains is preferred.
• Experience delivering AI/ML solutions in regulated environments with familiarity in validation, auditability, explainability and risk controls is a 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 10-12 + years of experience in Computer Science, IT or related field

OR

• Bachelors degree with 12-14 + 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.