Associate Machine Learning Engineer

The Associate Machine Learning Engineer position offers a unique opportunity to join a fun, innovative engineering team within the AI & Data Science (AI&D) organization.

You’ll work on next-generation capabilities and services in Applied AI & Automation using innovative COTS products, open-source software, frameworks, tools, and cloud computing services.

The role also emphasizes demonstrating these capabilities to support critical business operations and initiatives, in ensuring quality, compliance, and performance across Amgen’s Applied AI & Automation Footprint.

The role is responsible for building and scaling our AI and machines learning solutions from development to production.

Your expertise in MLOps will be essential in creating efficient and reliable ML pipelines.

The role represents working in the solution engineering and DevOps teams to lead the transition from Intelligent automation to Agentic Process Automation and ensures that strategy and implementation remain connected throughout the value stream.

The ideal candidate has experience in building AI & ML solutions, has excellent communication skills, and an understanding of Agile methodologies.

This role focuses on supporting the development, deployment, operation, and reliability of AI and LLM‐based solutions in production environments, working under guidance of senior engineers and platform teams.

The position emphasizes operational excellence, observability, and compliance for applied AI and GenAI systems.

• Collaborate with data scientists to develop, train, and evaluate machine learning models.
• Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring.
• Leverage cloud platforms (AWS, Databricks) for ML model development, training, and deployment.
• Develop solutions using DevSecOps framework that are secure, scalable, reliable, and aligned with enterprise architecture standards.
• Evaluate model performance using appropriate metrics and optimize models for accuracy and efficiency