Senior ML Engineer: Build & Scale Enterprise AI Platforms

Amgen; Lisboa; On-site; EUR 51,000 - 69,000; Full time

Job summary

AMGEN Capability Center Portugal in Lisbon seeks a Sr Machine Learning Engineer to design and scale end-to-end ML and GenAI platforms. You will create core services, governance, and secure APIs, partnering with DevOps, Security and Product teams to deliver enterprise-grade AI capabilities.

The role requires building and deploying models with Kubeflow/SageMaker Pipelines, containerization, and experience with Python/Java in cloud environments.

Qualifications

• 8+ years of experience in AI/ML for enterprise software.
• Strong experience with ML models, including transformers and time-series.
• Proven ability to deploy AI/ML services at scale and manage pipelines.

Responsibilities

• Engineer end-to-end ML pipelines for data ingestion, feature engineering and training.
• Harden research code into production-grade micro-services with Docker/Kubernetes.
• Expose secure APIs (REST/gRPC) for downstream apps to consume models.
• Build full-stack AI apps by integrating model services with lightweight UI/workflow layers.
• Instrument observability: metrics, tracing, drift detection and user analytics.
• Embed security and responsible-AI controls with partnerships across teams.
• Develop reusable platform components and evangelize best practices for velocity.
• Collaborate with data scientists to prototype and benchmark new algorithms.

Skills

Python

Java

Docker/Kubernetes

Cloud platforms

GenAI tooling

ML engineering

Education

Master's degree in Computer Science/IT

Bachelor's degree with 10+ years experience

Tools

Kubeflow

SageMaker Pipelines

OpenAI SDK

Job description

AMGEN Capability Center Portugal in Lisbon seeks a Sr Machine Learning Engineer to design and scale end-to-end ML and GenAI platforms. You will create core services, governance, and secure APIs, partnering with DevOps, Security and Product teams to deliver enterprise-grade AI capabilities.

The role requires building and deploying models with Kubeflow/SageMaker Pipelines, containerization, and experience with Python/Java in cloud environments.