Sr. Associate - Machine Learning Engineer

Career Category Human Resources Job Description 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 on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today. ABOUT THE ROLE ROLE DESCRIPTION: Amgen is hiring a hands-on Machine Learning Engineer (Level 4) to design, build and operationalize generative and agentic AI systems that are safe, auditable and production ready. You will own LLM and agent capabilities, evaluation frameworks and guardrails, and work with data science, product, platform and compliance partners to move models from prototype into trusted enterprise services. KEY RESPONSIBILITIES: •Design, implement and deploy LLM solutions and agent frameworks for production workflows, focusing on reliability, interpretability and safety. •Build and run evaluation pipelines: automated metrics, unit and integration test suites, adversarial/red-team tests, and human-in-the-loop evaluation. •Create and operationalize guardrails: prompt patterns, policy enforcement, input sanitization, output validation, data filtering and explainability tools. •Prototype and productionize agentic behaviours: RAGs, multi-step planners, tool-use interfaces, state management and safe action execution. •Implement MLOps best practices: model versioning, CI/CD for models, scalable serving, observability and cost controls. •Partner with security, privacy, regulatory and ethics teams to embed compliance and auditability into deployments. •Stay updated with the latest trends and advancements MUST-HAVE SKILLS: •5 to 9 years of applied machine learning or software engineering experience, including at least 2 years on production ML systems. •B.Tech, M.Tech or MS with specialization in Computer Science. •Strong hands-on experience with large language models: fine-tuning, instruction tuning, retrieval augmentation and embeddings. •Practical experience with agent frameworks and multi-step agent behaviours. •Solid software engineering skills in Python and deep learning frameworks such as PyTorch, JAX or TensorFlow. •Experience building evaluation suites, human-in-the-loop workflows and adversarial testing. •Demonstrated implementation of guardrails and safety mechanisms for NLP/LLM systems. •Experience deploying services in cloud environments and with container orchestration (Kubernetes) and model serving technologies. •Clear communicator able to translate prototypes into production solutions and to engage cross-functional stakeholders. GOOD-TO-HAVE SKILLS: •Prior work on Agentic AI, multi-agent systems, planners or applied agent safety research. •Experience with benchmarking and continuous evaluation tooling. •Background in regulated industries such as pharma or healthcare. •Familiarity with Traditional ML, data governance and model risk management. •Advanced technical credentialing or proven contributions to open-source or academic work in LLMs or agent systems. BEHAVIORS AND COMPETENCIES: •Pragmatic execution: delivers practical, well-engineered solutions that work in production. •Ownership: drives projects from design through sustained operations. •Security and compliance mindset: designs for auditability, traceability and risk reduction. •Collaborative: aligns product, platform, governance and data science stakeholders. •Continuous learner: keeps pace with LLM and agent research and applies best practices. .