we are looking for a MLOps Team Lead.
As an MLOps Team Leader , you will own our AI infrastructure and backend engineering efforts, leading a team at the intersection of systems design and machine learning to build the engineering systems that turn Deep Learning and Computer Vision research into reliable, scalable production features.
What Youll Be Doing:
Lead MLOps Engineering: Own the roadmap and execution for a team of backend and AI infrastructure/MLOps engineers, setting technical direction, removing blockers, and holding the bar on delivery quality.
Build Production AI Systems: Design and implement production-grade, end-to-end AI solutions, including agentic workflows, that integrate Deep Learning models and Computer Vision algorithms into real features. Own the offline experimentation and validation layer, including POC environments that let the Algo team move from research to production confidently.
Architect Data Platforms: Drive the design and implementation of scalable data platforms and pipelines that power AI capabilities reliably and with an eye on cost and scale.
Cross-functional Collaboration: Work closely with the Algorithms and Data teams to tackle complex, real-world problems, translating research into shippable, maintainable systems.
Elevate Engineering Standards: Champion Software Engineering and System Design best practices across the group, introducing the right methodologies, tooling, and culture of craft.
Who You Are:
Requirements: Professional Experience: At least 5 years of hands-on Software Engineering experience, with a minimum of 2 years in a team lead or managerial role.
Technical Proficiency:
Production Systems and DevOps: Proven experience building high-scale, production-grade systems on a cloud platform (AWS preferred), with solid command of DevOps practices including CI/CD, containerization, and observability.
Programming: Strong command of at least one programming language, with Python or Rust being a strong advantage.
AI/ML Systems: Solid understanding of the ML model lifecycle, including training, evaluation, deployment, and monitoring, with enough hands-on exposure to make good infrastructure decisions around it. Experience with TensorFlow, PyTorch, or Computer Vision concepts is an advantage.
AI-Augmented Development: Hands-on experience integrating AI coding tools into engineering workflows, with a genuine interest in expanding their use across the team.
Data Engineering: Hands-on experience with data pipelines and big data infrastructure.
Leadership and Mindset:
Engineering Quality: You hold a high bar for correctness, reliability, and maintainability, and you treat quality as a first-class engineering concern, not something retrofitted at the end of a sprint.
Team Builder: A people-first approach to leadership. You coach, you unblock, and you build psychological safety alongside technical excellence.
Engineering Judgment: A strong ability to self-learn, cut through ambiguity, and land well-reasoned technical decisions under pressure.
This position is open to all candidates.