Description:
This role can be based in the UK (Newcastle), Ireland (Dublin), or the United States (preferably East Coast). Candidates should reside within a reasonable distance of a Red Hat office in one of these locations.
What You Will Do
- Lead the architecture and implementation of MLOps/LLMOps systems within OpenShift AI, establishing best practices for scalability, reliability, and maintainability while actively contributing to relevant open source communities
- Design and develop robust, production-grade features focused on AI trustworthiness, including model monitoring, bias detection, and explainability frameworks that integrate seamlessly with OpenShift AI
- Drive technical decision-making around system architecture, technology selection, and implementation strategies for key MLOps components, with a focus on open source technologies like KServe and TrustyAI
- Define and implement technical standards for model deployment, monitoring, and validation pipelines, while mentoring team members on MLOps best practices and engineering excellence
- Collaborate with product management to translate customer requirements into technical specifications, architect solutions that address scalability and performance challenges, and provide technical leadership in customer-facing discussions
- Lead code reviews, architectural reviews, and technical documentation efforts to ensure high code quality and maintainable systems across distributed engineering teams
- Identify and resolve complex technical challenges in production environments, particularly around model serving, scaling, and reliability in enterprise Kubernetes deployments
- Partner with cross-functional teams to establish technical roadmaps, evaluate build-vs-buy decisions, and ensure alignment between engineering capabilities and product vision
- Provide technical mentorship to team members, including code review feedback, architecture guidance, and career development support while fostering a culture of engineering excellence
What You Will Bring
- 5+ years of software engineering experience, with at least 4 years focusing on ML/AI systems in production environments
- Strong expertise in Python, with demonstrated experience building and deploying production ML systems
- Deep understanding of Kubernetes and container orchestration, particularly in ML workload contexts
- Extensive experience with MLOps tools and frameworks (e.g., KServe, Kubeflow, MLflow, or similar)
- Track record of technical leadership in open source projects, including significant contributions and community engagement
- Proven experience architecting and implementing large-scale distributed systems
- Strong background in software engineering best practices, including CI/CD, testing, and monitoring
- Experience mentoring engineers and driving technical decisions in a team environment