MLOps Engineer

Fundamental; United Kingdom; On-site; GBP 90,000 - 150,000; Full time

Benefits offered by this job

Competitive compensation with salary &

Equity in a fast-growing AI startup

Comprehensive health coverage

Relocation support for office moves

Paid parental leave

Job summary

Fundamental is an AI research lab pioneering enterprise decision-making. You will develop scalable ML pipelines, CI/CD workflows, and robust model serving infrastructure to support ultra-low latency inference at scale.

Join a mission-driven team building evidence-backed systems, leveraging TorchServe, TensorFlow, and Triton, with Kubernetes, IaC, and observability tooling to optimize performance and reliability across large data volumes.

Qualifications

• Degree in CS/Engineering or equivalent practical experience.
• 5+ years as MLOps engineer or DevOps with ML platforms.
• Experience building MLOps infrastructure from ground up.
• Experience with model serving frameworks for high scalability and low latency inference.
• Experience building data pipelines for training and inference.
• Kubernetes on a major cloud provider with IaC (Terraform/Helm/GitOps).
• Strong Python, Bash, Go software engineering.
• AI/ML systems security, compliance, governance.
• Proficient with observability tools (Prometheus, Grafana, Datadog, OpenTelemetry).

Responsibilities

• 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 to efficiently handle large volumes of data

Skills

MLOps engineering

CI/CD pipelines

Model serving

Kubernetes

Python

Cloud platforms (AWS/GCP/Azure)

Data pipelines

Observability tools

Security/compliance

Education

Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

Tools

MLflow

WandB

TorchServe

TensorFlow Serving

Triton

KServe

Terraform

Helm

GitOps

Prometheus

Job description

Fundamental is an AI research lab pioneering the future of enterprise decision-making. Our flagship model, NEXUS is the world’s most powerful Large Tabular Model (LTM) - purpose-built for the structured records that contain trillions of dollars in business value. With $275m in funding from leading investors and trusted by Fortune 100 companies, Fundamental is giving businesses the Power to Predict.

At Fundamental, you’ll work on unprecedented technical challenges in foundation model development and build technology that transforms how the world’s largest companies make decisions. This is your opportunity to be part of a category-defining company from the ground-up. Join the team defining the future of enterprise AI.

Key Responsibilities

• 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 to efficiently handle large volumes of data

Must have

• 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
• 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
• A mission-driven, low-ego culture that values diversity of thought, ownership, and bias toward action