MLOps Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a MLOps Engineer based in United Kingdom.

Join a high-impact engineering team building the infrastructure that powers next-generation AI solutions for enterprise-scale decision-making.

In this role, you will design, deploy, and optimize production-grade machine learning systems that support the full ML lifecycle, from training to inference.

You'll collaborate with talented engineers to create highly scalable, reliable, and secure MLOps platforms capable of handling demanding workloads.

This is an opportunity to solve complex technical challenges, improve model performance at scale, and contribute to cutting-edge AI technologies in a fast-paced, collaborative, and remote-first environment.

The role offers significant ownership, modern cloud-native tooling, and the chance to shape the future of production AI systems.

• Accountabilities
• Develop, automate, and maintain scalable machine learning pipelines, CI/CD workflows, and orchestration frameworks to support efficient model development and deployment.
• Design and implement high-performance model serving infrastructure using industry-standard serving frameworks while optimizing inference for low latency and high throughput.
• Build reliable deployment strategies including A/B testing, canary releases, rollback mechanisms, and production validation processes.
• Create robust monitoring, logging, alerting, and observability solutions to ensure model reliability, performance, and operational excellence.
• Optimize infrastructure utilization by improving GPU efficiency, enabling autoscaling, and managing cloud resources effectively.
• Design and maintain feature stores, scalable data pipelines, and storage architectures capable of supporting large-scale training and inference workloads.
• Collaborate with engineering teams to continuously improve platform scalability, security, governance, and operational best practices.

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline, or equivalent practical experience.
• At least 5 years of experience in MLOps, Dev Ops, or related engineering roles supporting production machine learning environments.
• Proven experience designing and building MLOps infrastructure from the ground up using platforms such as MLflow, Weights & Biases, Kubeflow, or similar.
• Strong hands‑on experience with machine learning frameworks including Py Torch and Tensor Flow, as well as model serving technologies such as Torch Serve, Tensor Flow Serving, Triton, or KServe.
• Solid experience developing and managing scalable data pipelines, Kubernetes environments, cloud infrastructure (AWS, GCP, or Azure), and Infrastructure as Code solutions including Terraform, Helm, or Git Ops.
• Strong programming skills in Python, Bash, and Go, with a focus on maintainable, scalable, and production‑quality software.
• Knowledge of AI system security, model governance, compliance, monitoring, and observability tools such as Prometheus, Grafana, Datadog, or Open Telemetry.
• Experience with Fast API, Databricks, Snowflake, SRE practices, or cloud security certifications is considered an advantage.

Benefits

• Competitive salary and equity package.
• Comprehensive healthcare coverage for employees and eligible dependents.
• Paid parental leave supporting all paths to parenthood, including adoption and surrogacy.
• Relocation assistance for employees joining one of the company's office locations where applicable.
• Fully remote work within Europe.
• Opportunity to work on cutting‑edge AI technologies with significant technical ownership.
• Inclusive, collaborative, and mission‑driven engineering culture focused on innovation, learning, and professional growth.
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