Applied AI - Machine Learning Engineer

Role Description:

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

The Machine Learning Engineer is responsible for designing, developing, deploying, and supporting AI/ML solutions from experimentation to production. This role plays a key part in operationalizing machine learning and GenAI use cases through robust MLOps practices, reusable engineering patterns, and secure cloud-native implementations. The engineer will collaborate closely with architects, product teams, and platform teams to deliver scalable, reliable, and responsible AI solutions that align with enterprise architecture and business goals. The ideal candidate has hands-on experience in ML engineering, model deployment, cloud platforms, and modern software engineering practices, along with strong communication skills and a good understanding of Agile methodologies.

Roles & Responsibilities:

• Design, build, and deploy machine learning and GenAI solutions that address business needs and integrate with enterprise platforms.
• Collaborate with data scientists, product owners, solution architects, and platform engineers to move models from experimentation to production.
• Build and maintain scalable MLOps pipelines, including data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining.
• Develop secure, scalable, reliable, and maintainable solutions aligned with DevSecOps and enterprise architecture standards.
• Leverage cloud platforms such as AWS and Databricks for model development, orchestration, deployment, and monitoring.
• Implement model observability, explainability, performance tracking, and operational health checks to ensure reliable production performance.
• Support development of GenAI and agent-enabled solutions using modern frameworks, vector stores, RAG pipelines, and orchestration tooling where applicable.
• Optimize ML systems for performance, cost, scalability, and maintainability across environments.
• Contribute reusable components, templates, and engineering best practices that improve delivery speed and consistency across teams.
• Develop and execute unit tests, integration tests, and validation strategies to ensure solution quality and resilience.
• Create and maintain technical documentation covering architecture, design, deployment, operations, and support procedures.
• Troubleshoot production issues, resolve technical challenges, and provide ongoing support and maintenance for deployed solutions.
• Analyze user feedback, operational metrics, and support trends to identify improvement opportunities and enhance solution effectiveness.
• Stay current with emerging AI/ML, GenAI, agentic automation, and MLOps technologies, and evaluate their applicability within Amgens ecosystem.
• Guide junior engineers and contribute to team learning through code reviews, knowledge sharing, and engineering standard methodologies.

Basic Qualifications and Experience:

[GCF Level 4]

• Any degree and 5 to 9 years of experience with software development technologies

Functional Skills:

Must-Have Skills:

• Strong foundation in machine learning algorithms, model development lifecycle, and production deployment patterns.
• Hands-on experience with MLOps tools and practices for model training, deployment, monitoring, and retraining.
• Proficiency in Python and experience with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch.
• Experience with cloud platforms such as AWS and/or Databricks for developing and managing ML workloads.
• Strong understanding of software engineering, CI/CD, DevSecOps, testing, and version control in AI/ML delivery.
• Experience building scalable ML/GenAI solutions using containerization, orchestration, and distributed data processing frameworks.

Good-to-Have Skills:

• Experience with GenAI patterns such as RAG, vector databases, prompt engineering, and agent frameworks.
• Knowledge of monitoring and observability tools for production M…