Senior Machine Learning Platform Engineer

About this role

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Skills you'll use

What you'll do

• We are seeking a Senior Machine Learning Platform Engineer to design, build and scale enterprise-grade machine-learning and generative-AI platform capabilities.
• This role sits at the intersection of platform engineering, software engineering, MLOps and GenAI engineering. Rather than primarily developing individual machine-learning models or business-specific AI applications, you will build the shared platform capabilities, APIs, automation and engineering patterns that enable teams to develop, evaluate, deploy, govern and operate AI solutions at scale.
• You will work closely with data scientists, ML engineers, application teams, DevOps, Security, Compliance and Product teams to create a secure, reliable and frictionless AI developer experience. The role combines hands-on engineering with technical leadership, helping define platform standards, reusable patterns and architecture for enterprise AI systems.
• Roles & Responsibilities
• Design and build reusable ML and GenAI platform capabilities that support model development, experimentation, evaluation, deployment and production operations across multiple teams and use cases.
• Build self-service platform services, APIs and automation that abstract infrastructure complexity and enable developers to provision and consume AI capabilities consistently.
• Develop and maintain MLOps capabilities including experiment tracking, model and prompt registries, evaluation frameworks, deployment workflows and automated promotion across environments.
• Build model-serving and inference capabilities that support classical ML models, deep-learning models and LLMs through scalable REST, gRPC or event-driven interfaces.
• Develop platform capabilities for GenAI and agentic systems, including model access, prompt management, embeddings, vector search, Retrieval-Augmented Generation, tool integration and agent frameworks.
• Engineer integrations with major cloud-based AI and data platforms, using APIs, SDKs and managed services to provide reusable enterprise capabilities.

Who can apply

• 3–5 years of experience in machine learning engineering, ML platform engineering, MLOps, backend engineering, cloud engineering or enterprise AI systems.
• Strong software-engineering fundamentals with experience building production-grade distributed services, APIs and reusable libraries.
• Strong programming skills in Python; experience with Java or another enterprise programming language is preferred.
• Experience designing and developing REST APIs, microservices and backend platform services.
• Hands-on experience with Docker and Kubernetes or equivalent containerization and orchestration technologies.
• Strong understanding of MLOps and GenAIOps concepts, including experiment tracking, model lifecycle management, evaluation, deployment, monitoring and reproducibility.
• Experience with platforms and technologies such as MLflow, Kubeflow, SageMaker, Databricks or equivalent ML platforms.
• Experience building or integrating model-serving infrastructure for ML models and/or LLM-based applications.
• Strong understanding of modern GenAI architecture patterns, including LLM APIs, prompt management, embeddings, vector databases, RAG and agent-based systems.

About Amgen

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