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MLOps Engineer (Kubernetes, Cloud, ML Workflows)

FitNext Co.
London
1 month ago
Applications closed

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MLOps Engineer

MLOps Engineer

MLOps Engineer

MLOps Engineer (Kubernetes, Cloud, ML Workflows)

Senior MLOps Engineer London

Senior MLOps Engineer



On-Site MLOps Engineer (Kubernetes, Cloud, ML Workflows)

London, UK – Soho (next to Tottenham Court Road) | Contractor | Start: ASAP

About the Role

Strong MLOps engineer with exposure in high-volume systems to help implement best practices and scale MLOps practice for a global brand. Must have strong on-prem and Kubernetes experience. This role includes an on-call rotation. You'll collaborate with ML engineers and product teams, bringing infrastructure excellence and MLOps innovation to a rapidly scaling environment.


⚠ This is a hybrid role with 3 days on-site in London (Soho, near Tottenham Court Road). Applications are considered only from candidates able to work on-site.



Required Skills




  • Strong on-prem as well as AWS cloud experience




  • Expertise managing GPU-enabled Kubernetes clusters for scalable and distributed systems




  • Deep understanding of the ML lifecycle in production, including deployment, versioning, and monitoring




  • Proficiency in Python or Go (3+ years experience required), focused on building automation and tooling for ML




  • Experience with CI/CD tools like ArgoCD or GitHub Actions for ML workflows




  • Skilled in using observability tools such as Prometheus, Grafana, and cloud-native stacks




  • Hands-on experience with Docker and container orchestration in cloud-native or hybrid environments




  • Familiarity with Infrastructure-as-Code tools, especially Terraform




  • Capable in cloud environments (AWS, GCP, or Azure) with exposure to AI/ML services




  • Confident in incident response, with experience participating in on-call rotations





Bonus Skills




  • Experience with AWS ML services like SageMaker and Bedrock




  • Enthusiasm for exploring emerging MLOps tools and practices




  • Demonstrated ability to support data scientists by building scalable ML experimentation infrastructure




Experience & Requirements




  • 7+ years of DevOps experience with high-scale, distributed systems




  • 2+ years of hands-on experience in MLOps environments




  • Experience managing on-prem systems and Kubernetes clusters




  • 3+ years of Python or Go experience




  • Familiarity with CI/CD tools for ML workflows and Infrastructure-as-Code





Hiring Process




  • 1 technical interview with BarRaiser




  • 2 technical rounds




  • 1 final HR round





Other Details







  • Rate: Up to $65/hr




  • Long-term contractor engagement (commission paid over 10 months)

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