Fundamental
MLOps Team Lead
Europe, Remote. Posted 4 months ago
The role
You will lead and mentor a team of MLOps engineers. You will define the MLOps roadmap and architect scalable machine learning pipelines and infrastructure.
What you will do
- Lead and mentor a team of MLOps engineers
- Define and drive the MLOps roadmap
- 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
- Collaborate on inference architecture strategy
- Design and maintain feature stores, robust data pipelines, and scalable storage solutions
- Collaborate with research teams to bridge the gap between experimentation and production
- Define logging, alerting, and monitoring strategy
Required qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
- 7+ years of experience in MLOps
- 5+ years in a technical leadership role
- Strong software engineering skills in Python
- 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)
- 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)
Nice to have
- Experience with workflow orchestration tools (Kubeflow, Airflow, Argo Workflows)
- Experience with FastAPI and backend applications
- Familiarity with data platforms like Databricks or Snowflake
- Experience with LLM/foundation model serving and optimization
- Exposure to SRE practices or cloud security certifications
- Experience scaling ML infrastructure for AI startups
Benefits
- Competitive compensation with salary and equity
- Comprehensive health coverage for you and your dependents
- Paid parental leave for all new parents, inclusive of adoptive and surrogate journeys
- Relocation support for employees moving to join the team in one of our office locations
About the company
Fundamental is an AI research lab pioneering the future of enterprise decision-making. Their flagship model, NEXUS, is a Large Tabular Model built for structured records.