Lead MLOps Engineer

In this role, you lead the MLOps direction for the AI for Engineering team and translate experiments into reliable production ML systems. You shape the platform, set engineering standards, and mentor engineers to raise the bar. You build and own key production ML infrastructure, tooling, and observability while collaborating with leadership to align priorities. This is a hands-on leadership role at scale, driving measurable impact on infrastructure design and sustainability goals.

• medical
• dental
• vision
• life
• AD&D
• disability benefits

• Define and drive the MLOps vision, platform roadmap, and engineering standards across AI for Engineering
• Provide technical leadership and mentoring through design/code reviews and knowledge sharing
• Architect and own production ML infrastructure for training, deployment, serving, and monitoring at scale
• Set CI/CD, automation, model delivery, and ML lifecycle standards used across teams
• Remain hands-on by building Python services, APIs, and platform tooling for production ML workloads
• Ensure reliability, observability, performance, availability, security, and cost control with SLOs and on-call practices
• Lead technical responses to complex production issues and implement lessons learned
• Make architecture and build-vs-buy decisions, aligning engineers, data scientists, and product teams
• Shape engineering culture, best practices, and technical hiring as the team scales

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
• Significant production ML deployment/maintenance experience including ML platform ownership
• Technical leadership experience influencing engineers without formal authority
• Deep experience with ML pipelines, deployment, serving, monitoring, and lifecycle infra
• Strong Python skills and experience with FastAPI, Flask, or Django
• Hands-on experience with Docker, CI/CD, and Azure cloud environments
• Track record of making architecture decisions for reliability and scalability
• Experience defining engineering standards and operational processes
• Excellent communication and stakeholder management skills
• Collaborative, high-ownership engineering style

• communication
• stakeholder management
• collaboration
• Python
• FastAPI/Flask/Django
• Docker