MLOps Team Lead
Fundamental, Europe +1
What you will do
- Lead and mentor a team of MLOps engineers, fostering technical growth and a culture of operational excellence
- Define and drive the MLOps roadmap, aligning infrastructure capabilities with Research, Engineering and product objectives
- Establish best practices, standards, and processes for ML infrastructure, deployment, and operations
- Own technical decision-making for ML infrastructure architecture and tooling choices
- Architect and oversee scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks
- Partner with the model-serving team on serving infrastructure strategy (Triton, TorchServe, TensorFlow Serving, KServe), ensuring training-side decisions (checkpoint formats, export pipelines, resource footprint) don't create friction downstream
- Collaborate on inference architecture strategy, bringing training-side context on model size, latency/throughput tradeoffs, and hardware requirements into early research decisions
- Design and maintain feature stores, robust data pipelines, and scalable storage solutions to efficiently handle large volumes of data
- Collaborate with research teams to bridge the gap between experimentation and production
- Define logging, alerting, and monitoring strategy to track model performance, drift, and system reliability
What you need
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
- 7+ years of experience in MLOps, with 5+ years in a technical leadership role
- Strong software engineering skills in Python, with experience in Bash and/or Go
- Proven track record of building and leading high-performing MLOps or infrastructure teams
- Experience building and designing MLOps infrastructure from the ground up
- Deep experience with MLOps platforms (MLflow, WandB, etc.) and frameworks (PyTorch, TensorFlow, etc.)
- Deep experience with model serving frameworks (Triton, TorchServe, TensorFlow Serving, KServe) for high scalability and low latency inference
- Experience building and managing data pipelines to support both model training and inference
- Good experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (Terraform, Helm, GitOps)
- Proficient with observability and monitoring tools (Prometheus, Grafana, Datadog, OpenTelemetry)
- Excellent communication skills with ability to translate between research and production contexts