Principal Software Engineer – AI Experiment Tracking

Role Overview

• Architect and lead implementation of new features and solutions for MLFlow on Red Hat OpenShift AI
• Provide technical vision and leadership on critical and high-impact projects, ensuring quality, scalability, and reliability across systems
• Innovate in the MLOps domain by participating in upstream communities, particularly Kubeflow and MLFlow
• Establish and champion quality engineering standards across teams, ensuring robust testing practices, CI/CD pipelines, and quality-first culture at scale
• Ensure non-functional requirements including security, resiliency, performance, and maintainability are consistently met
• Write and review complex test strategies, frameworks, and automation approaches that raise the bar for quality across the organization
• Contribute to a culture of continuous improvement by sharing recommendations and technical knowledge with team members
• Collaborate with product management, other engineering, and cross-functional teams to analyze and clarify business requirements
• Communicate effectively with stakeholders and leadership to provide visibility and influence decision-making
• Give thoughtful and prompt code reviews, modeling high standards of quality, maintainability, and design
• Represent RHOAI in external engagements including industry events, customer meetings, and open source communities
• Mentor, influence, and coach a distributed team of engineers, developing future technical leaders and instilling strong engineering discipline
• Explore and experiment with emerging AI technologies relevant to software development, proactively identifying opportunities to incorporate new AI capabilities into existing workflows and tooling

Requirements

• Advanced experience developing applications in Go or Python, or another programming language
• Advanced experience with AI experiment tracking tools such as MLFlow, Weights and Biases, or ClearML
• Advanced experience in Kubernetes, OpenShift, or other cloud-native technologies
• Expertise in defining, scaling, and enforcing testing strategies, automation frameworks, and CI/CD pipelines across large, distributed systems
• Ability to quickly learn and guide others on using new tools and technologies, including AI-assisted development tools
• Experience with source code management tools such as Git
• Proven ability to innovate and a passion for staying at the forefront of technology, including quality engineering best practices
• Excellent system understanding and troubleshooting capabilities, with a focus on scalability, reliability, and performance
• Technical leadership acumen in a global team environment, including mentoring and coaching engineers at multiple levels
• Excellent written and verbal communication skills.

Tech Stack

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

Benefits

• Flexible work arrangements
• Professional development opportunities