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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Principal Machine Learning Engineer - **Company:** Amgen - **Location:** Washington, DC, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Agile Methodology, Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Software as a Service, Datacards, DevOps, Github, Python (Programming Language), Machine Learning, Platform as a Service (PAAS), Azure Machine Learning, Scaled Agile Framework, Google Cloud, Enterprise Software Applications, Feature Engineering, Data Ingestion, Large Language Models, Multi-Agent Systems, Prompt Engineering, Deep Learning, Containerization, Material UI, Kubernetes, Information Technology, Machine Learning Operations, Docker, Natural Language Generation - **Published:** September 28, 2026 - **Apply:** https://dejobs.org/x/x/EB86571A22CA4C3281A4819E65BE7B38/job/ ## About the Role Doctorate degree and 2 years of Machine Learning Engineer experience OR Master's degree and 6 years of Machine Learning Engineer experience OR Bachelor's degree and 8 years of Machine Learning Engineer experience OR Associate's degree and 10 years of Machine Learning Engineer experience OR High school diploma / GED and 12 years of Machine Learning Engineer experience In addition to meeting at least one of the above requirements, you must have a minimum of 2 years experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation or resources. Your managerial experience may run concurrently with the required technical experience referenced above * 3-5 years in AI/ML and enterprise software. * Strong 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, LangGraph, 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., * 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 10-12 + years of experience in Computer Science, IT or related field OR * Bachelor's degree with 12-14 + 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. ## Description We are seeking a Principal Machine Learning Engineer -Amgen's most senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI solutions. Sitting at the intersection of engineering excellence and data-science enablement, you will develop, deploy and monitor models-classical ML, deep learning and LLMs-securely and cost-effectively. Acting as a "player-coach," you will establish AI solution strategy, define technical standards, and partner with DevOps, Security, Compliance and Product teams to deliver a frictionless, enterprise-grade AI solutions., * Build end-to-end ML pipelines -data ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotion-using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks. * 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. * Establish observability, SLOs, and safe deploys (blue-green/canary, shadow, rollbacks) with incident runbooks. * Lead rigorous evaluation (offline/online, A/B), drift detection, and automated retraining. * Architect LLM/RAG with prompt management, safety guardrails, and optimized inference. * Enforce data quality , lineage, and model/data cards; apply privacy-preserving techniques where needed. * Contribute reusable ML/GenAI 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. * Prototype and benchmark new algorithms , offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs. * Translate domain needs (R&D, Manufacturing, Commercial) into roadmaps; mentor teams and communicate trade-offs.