Description
**ABOUT THE ROLE**
**Role Description:**
We are seeking a **Sr Machine Learning Engineer** —Amgen’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 models—classical ML, deep learning and LLMs—securely 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 pipelines** —data ingestion, feature engineering, training, hyper-parameter optimization, evaluation, registration and automated promotion—using 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 scale** —selecting 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 observability** —real-time metrics, distributed tracing, drift & bias detection and user-behavior analytics—enabling 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 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.
+ **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 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, 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.
**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.
