The Applied AI - Machine Learning Engineer role involves designing, developing, deploying, and supporting AI/ML solutions from experimentation to production, using innovative COTS products, open-source software, frameworks, tools, and cloud computing services. 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.
Requirements
- 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 Amgen’s ecosystem.
- Guide junior engineers and contribute to team learning through code reviews, knowledge sharing, and engineering standard methodologies.
