Job Details

Sr Machine Learning Engineer

We are seeking a Sr Machine Learning EngineerAmgen's senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI platforms. Sitting at the intersection of engineering excellence and data-science enablement, you will design the core services, infrastructure, and governance controls that allow hundreds of practitioners to prototype, deploy, and monitor modelsclassical ML, deep learning, and LLMssecurely and cost-effectively. Acting as a "player-coach," you will establish platform strategy, define technical standards, and partner with DevOps, Security, Compliance, and Product teams to deliver a frictionless, enterprise-grade AI developer experience.

Roles & Responsibilities:

• Engineer end-to-end ML pipelinesdata ingestion, feature engineering, training, hyper-parameter optimization, evaluation, registration, and automated promotionusing Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks.
• Harden research code into production-grade micro-services, packaging models in Docker/Kubernetes and exposing secure REST, gRPC, or event-driven APIs for consumption by downstream applications.
• 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.
• Optimize performance and cost at scaleselecting appropriate algorithms (gradient-boosted trees, transformers, time-series models, classical statistics), applying quantization/pruning, and tuning GPU/CPU auto-scaling policies to meet strict SLA targets.
• Instrument comprehensive observabilityreal-time metrics, distributed tracing, drift & bias detection, and user-behavior analyticsenabling rapid diagnosis and continuous improvement of live models and applications.
• Embed security and responsible-AI controls (data encryption, access policies, lineage tracking, explainability, and bias monitoring) in partnership with Security, Privacy, and Compliance teams.
• Contribute reusable platform componentsfeature stores, model registries, experiment-tracking librariesand 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.
• Partner with data scientists to prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs.

Must-Have Skills:

• 3-5 years in AI/ML and enterprise software.
• Comprehensive command of machine-learning algorithmsregression, 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, 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.
• Master's degree in computer science and/or Data Science.
• Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.

Education and Professional Certifications:

• Master's degree with 8+ years of experience in Computer Science, IT, or related field.
• OR
• Bachelor's degree with 10+ 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.