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
Responsible for the safe, auditable, and reliable release of Kubernetes-native AI platform components, with strong emphasis on progressive delivery, operational resilience, and supply-chain integrity.

Requirements

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

Benefits

Comprehensive medical, dental, and vision coverage
Flexible Spending Account - healthcare and dependent care
Health Savings Account - high deductible medical plan
Retirement 401(k) with employer match
Paid time off and holidays
Paid parental leave plans for all new parents
Leave benefits including disability, paid family medical leave, and paid military leave
Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more!

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonMLOpsKubernetesKServeKubeflowMLflowmodel monitoringbias detectionexplainability frameworksCI/CD

Soft Skills

technical leadershipmentoringcollaborationcommunicationproblem-solvingdecision-makingcode reviewarchitectural reviewtechnical documentationengineering excellence