Machine Learning Engineer

Career Category Information Systems Job Description Machine Learning Engineer, AI Studio CAREER LEVEL: GCF 4 – Senior Associate CAREER TRACK: Individual Contributor PRIMARY SCOPE: Independent ownership of defined production ML/AI components ORGANIZATION: Applied AI | AI Studio ABOUT AMGEN Amgen harnesses the best of biology and technology to fight the world’s toughest diseases and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains at the cutting edge of innovation, using technology and human genetic data to push beyond what is known today. ABOUT THE ROLE Role Description: The Machine Learning Engineer offers a unique opportunity to join a fun, innovative engineering team within the AI & Data Science (AI&D) - organization. We are the Applied AI team (AI Studio). AI Studio is Amgen’s enterprise engine for turning high-value business challenges into scalable AI products. We partner with key business partners across the company to identify the right opportunities, shape them into actionable use cases, and design, build, and launch AI products responsibly. Our work spans the full lifecycle from early discovery and rapid prototyping to production deployment, reuse across the enterprise, and measurable business impact. Y ou will independently own defined production components within enterprise AI products and automation solutions. Your remit may include a model or inference service, data or knowledge pipeline, retrieval component , agent tool, evaluation module, API, workflow or monitoring capability. You will design, release, diagnose and support the component while connecting technical measures to user and workflow outcomes. Within Applied AI, AI Studio turns prioritized business demand into governed, reusable AI assets with accountable ownership and measurable value across software, data, automation, machine learning, Generative AI, RAG, bounded agents, evaluation, observability and lifecycle operations. Roles & Responsibilities: Define component boundaries, intended use, acceptance criteria, non-functional requirements, decision consequences, support expectations and technical estimates with product and architecture partners. Design and implement maintainable Python, SQL, API, data, model, retrieval, agent-tool and workflow components with clear contracts, configuration, testing, error handling and documentation. Apply EDA, feature engineering, supervised or unsupervised methods, baselines, cross-validation, leakage prevention, calibration, subgroup, threshold, explainability and error analysis where relevant. Build GenAI, NLP, RAG and bounded agent components using structured output, embeddings, hybrid search, reranking, provenance, citations, permissions, approvals, retries and recoverable failure behavior . Engineer batch or event-driven data, document, feature, embedding, label and evaluation pipelines with schema validation, lineage, provenance, access control and consistency checks. Define representative evaluation for model quality, uncertainty, retrieval, grounding, citations, task success, tool correctness, safety, latency, cost and user impact. Release and support components using cloud services, containers, CI/CD, versioning, monitoring, rollback, incident response and runbooks; lead diagnosis of moderately complex failures. Apply security, privacy, Responsible AI, validation, auditability, human oversight and applicable GxP controls; contribute reusable assets and guide Associate engineers on familiar work. Basic Qualifications and Experience: Bachelor’s/ Master’s degree and 5 to 9 years of Computer Science, IT or related field experience . Functional Skills: Production software and AI/ML system design: Python and SQL modules, APIs, background jobs, event flows, testing, performance, observability, source control and maintainable failure semantics. Statistics, modeling and experimentation: EDA, feature engineering, classification, regression, clustering, ensembles, cross-validation, leakage prevention, calibration, uncertainty and decision-aware error analysis. GenAI, RAG, knowledge and agents: Prompt and context management, structured output, chunking, embeddings, hybrid retrieval, reranking, citations, access-aware retrieval, tool schemas and human approval. Data, knowledge and cloud-scale systems: Batch and event-driven pipelines, contracts, lineage, provenance, relational/document/graph/vector stores, APIs, containers, Spark or Databricks and cloud-native