Role overview
This position is part of the Enterprise Services – Data & AI organization within RTX Corporate, supporting the company’s mission to develop and scale AI capabilities responsibly and securely across the enterprise.
The Machine Learning Engineer designs and implements automation, observability, and lifecycle management pipelines for AI and machine learning applications. This role ensures that models and AI services operate reliably, securely, and cost-effectively across RTX environments.
What you'll work on
- Build and maintain CI/CD pipelines for AI model deployment and lifecycle management.
- Automate monitoring and telemetry for AI services using OpenTelemetry, MLflow, and Databricks.
- Productionize proof-of-concept (POC) code and integrate it into production systems.
- Implement access controls, cost tracking, and policy-as-code checks for production AI workloads.
- Develop dashboards and automated alerts to monitor system health, latency, and model performance.
- Collaborate with Platform and Applied AI Engineers to build reliable, high-performance production services and embed observability and governance into production pipelines.
- Ensure reproducibility, observability, and compliance of AI workflows across environments.
- Design and build scalable production pipelines that are highly performant, cost-efficient, and production-ready, ensuring seamless integration with existing systems and workflows.
- Continuously optimize infrastructure and workflows to support scaling of AI applications in dynamic enterprise settings.
What we're looking for
- Experience with policy-as-code or governance automation frameworks (Open Policy Agent or equivalent).
- Background in regulated environments (aerospace, defense, finance, or healthcare).
- Understanding of Responsible AI practices, model versioning, and auditability.
Tags & focus areas
Used for matching and alerts on DevFound Fulltime Remote Machine Learning Ai