Senior Software Engineer – AI Eval, Safety

Role Overview

• 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

Tech Stack

• Distributed Systems
• Kubernetes
• Open Source
• OpenShift
• Python

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!