Description

The Senior Machine Learning Platform Engineer will design, build, and scale enterprise-grade machine-learning and generative-AI platform capabilities. The role focuses on shared platform services, APIs, automation, MLOps, model serving, GenAI and agent systems, observability, security, governance, CI/CD, and technical leadership rather than individual model development. It requires 3–5 years of relevant experience, strong Python and software-engineering skills, cloud and containerization expertise, and familiarity with ML platforms, GenAI architectures, and enterprise AI systems.

Required Skills

Python
REST APIs
Docker
Kubernetes
MLOps
GenAI
LangChain
AWS
Terraform
Observability
CI/CD

Additional Information

Required Previous Experiences: 3–5 years of experience in machine learning engineering, ML platform engineering, MLOps, backend engineering, cloud engineering or enterprise AI systems.
Experience building production-grade distributed services, APIs and reusable libraries.
Experience designing and developing REST APIs, microservices and backend platform services.
Hands-on experience with Docker and Kubernetes or equivalent containerization and orchestration technologies.
Experience with MLflow, Kubeflow, SageMaker, Databricks or equivalent ML platforms.
Experience building or integrating model-serving infrastructure for ML models and/or LLM-based applications.
Experience integrating cloud-based AI/ML SaaS and PaaS services and building abstractions for broader developer consumption.
Experience with at least one major cloud platform such as AWS, Azure or GCP.
Familiarity with CI/CD, Git-based development, automated testing and release-management practices.
Understanding of observability and distributed-system concepts, including logging, metrics, tracing, retries, timeouts and failure handling.
Understanding of enterprise security patterns, including authentication, authorization, RBAC, OAuth/OIDC, secrets management and encryption.
Familiarity with data-governance and responsible-AI concepts, including lineage, explainability, access controls, model evaluation and bias monitoring.
Experience working with relational and NoSQL databases and structured or unstructured data systems.
Ability to design reusable platform abstractions rather than solving requirements through one-off application implementations.
Ability to evaluate architectural options and communicate trade-offs across performance, scalability, reliability, security and cost.
Strong collaboration and communication skills, with the ability to work across data science, engineering, infrastructure, security and product teams.
Preferred Qualifications: Experience with Java or another enterprise programming language.
Experience building internal developer platforms, ML platforms or self-service engineering platforms.
Experience with Infrastructure as Code such as Terraform.
Experience designing multi-tenant platform services with resource isolation, quotas, governance and usage attribution.
Experience with feature stores, model registries, evaluation platforms, AI gateways or centralized model-serving architectures.
Familiarity with event-driven architectures, message queues and asynchronous processing.
Experience defining technical standards, architecture patterns and reusable engineering frameworks across multiple teams.
Translation Company Name: Amgen

About Amgen

Amgen

life-saving medicinesThousand Oaks, California

Amgen discovers, develops, manufactures and delivers innovative medicines to fight some of the world’s toughest diseases. Harnessing the best of biology and technology, Amgen reaches millions of patients with its medicines, helping make people’s lives easier, fuller and longer.

Founded: 1980

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Senior Machine Learning Platform Engineer at Amgen

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