Fundamental
MLOps Engineer
Europe, Remote. Posted 3 months ago
The role
You will develop and manage scalable machine learning pipelines, CI/CD workflows, and orchestration frameworks. You will design model serving infrastructure and scalable inference architectures to support foundation model development.
What you will do
- Develop and manage scalable, automated machine learning pipelines, CI/CD workflows, and orchestration frameworks
- Design and implement robust model serving infrastructure using platforms like TorchServe, TensorFlow, Triton etc.
- Develop scalable inference architectures optimized, with ultra-low latency and high throughput
- Ensure seamless model deployment by implementing A/B testing, canary releases, and rollback capabilities
- Develop logging, alerting, and monitoring solutions to track model development, and reliability
- Improve GPU usage, enable autoscaling, and streamline resource allocation to boost efficiency
- Design, implement, and maintain feature stores, robust data pipelines, and scalable storage solutions
Required qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
- 5+ years of experience as MLOps engineer or DevOps roles, working with MLOps platforms (MLflow, WandB etc..) and frameworks (PyTorch, TensorFlow etc..)
- Experience building and designing MLOps infrastructure from the ground up
- Experience with model serving frameworks (TorchServe, TensorFlow Serving, Triton, KServe etc..) for high scalability and low latency inference
- Experience in building and managing data pipelines to support both model training and inference
- Experience with Kubernetes on a major cloud provider (AWS, GCP, or Azure) and with infrastructure as code (e.g. Terraform, Helm, GitOps)
- Strong software engineering skills in Python, Bash, and Go, with a focus on writing clean, maintainable, and scalable code
- Experience in AI/ML systems security, compliance, and model governance
- Proficient with observability and monitoring tools, such as Prometheus, Grafana, Datadog, and OpenTelemetry
Nice to have
- Experience with ML workflow tooling (MLflow, Kubeflow, or similar)
- Experience with FastAPI and Backend applications
- Familiarity with data platforms like Databricks or Snowflake
- Exposure to SRE practices or cloud security certifications
- Hands-on experience with Prometheus, Grafana, or Datadog
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.