Sr Machine Learning Engineer

Career Category Information Systems Job Description Senior Machine Learning Engineer, AI Studio CAREER LEVEL: GCF 5 – Specialist CAREER TRACK: Individual Contributor PRIMARY SCOPE: End-to-end ownership of a small AI asset or substantial technical workstream 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 Senior Machine Learning Engineer position 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 be part of AI Studio and define and own AI asset s or substantial technical workstream from problem framing through architecture, model and system development, evaluation, launch, stabilization, support transition, adoption and measurable outcome. You will remain hands-on while leading decisions across software, statistics, classical ML, deep learning, NLP, GenAI, RAG, bounded agents, data and knowledge pipelines, APIs, MLOps / LLMOps , security, governance and operations. Within Applied AI - AI Studio turn 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 the user, workflow, decision, intended use, baseline, value hypothesis, acceptance criteria, adoption path, operating owner and measurable technical and business outcomes. Map rules, exceptions, data dependencies and human decision points before selecting deterministic automation, classical ML, deep learning, GenAI, RAG, agents or a manual approach. Own production architecture across data, feature and knowledge pipelines, models, retrieval, agents, APIs, persistence, workflows, user experience, security zones and human review. Lead hands-on development of production software, EDA, feature engineering, predictive models, deep-learning or NLP components, inference services, RAG, agent tools and workflow orchestration. Establish baselines, experiment design, leakage controls, uncertainty, calibration, subgroup and robustness checks, gold sets, error taxonomies, expert adjudication and release thresholds. Establish MLOps / LLMOps for lineage, reproducibility, versioning, CI/CD, canary or shadow release, observability, drift monitoring, SLOs, rollback, incidents, disaster recovery, capacity, cost and runbooks. Coordinate security, privacy, Responsible AI, Quality, legal, model-risk and GxP controls; create reusable capabilities, measure adoption and value, mentor engineers and improve delivery practices. Basic Qualifications and Experience: • Bachelor’s/ Master’s degree with 8 - 13 years of experience in Computer Science, IT or related field. Functional Skills: Advanced software and AI/ML system design: Production Python and SQL, APIs, distributed or event-driven services, data persistence, testing, performance, repository governance, design review and end-to-end architecture. Advanced statistics, ML and experimental design: EDA, feature engineering, supervised and unsupervised learning, predictive modelling , ensembles, anomaly detection, calibration, uncertainty, robustness, explainability and causal reasoning where justified. Deep learning, NLP and foundation models: Selection and production use of neural, transformer, embedding, vision, document and multimodal approaches, including fine-tuning versus prompting, latency, privacy and cost trade-offs. <