Jobs / EPA***

MLOps Engineer

EPA*** · London, ENG, United Kingdom
London, ENG, United KingdomHybrid
Remuneration
Not specified
Location
London, ENG, United Kingdom
Visa sponsorship
Sponsors visa

Job summary

The MLOps Engineer role involves building, deploying, and maintaining production-ready machine learning solutions with a focus on MLOps practices such as CI/CD, model serving, and cloud infrastructure. Responsibilities include developing CI/CD pipelines, managing scalable deployments with Kubernetes, and collaborating with engineering and data science teams to enhance AI capabilities and operational scalability.

Qualifications

  • Experience in delivering machine learning or MLOps systems into production environments
  • Proficiency in Python for building services, APIs, scripts and CI/CD automation
  • Working knowledge of modern MLOps stacks including experiment tracking and artifact management
  • Hands-on experience with orchestration tools (e.g., Kubeflow, Apache Airflow, Metaflow or Prefect)
  • Demonstrated skills with Docker, Kubernetes and distributed deployments
  • Practical knowledge of Infrastructure-as-Code (Terraform) and a major cloud provider (AWS, Azure or GCP)
  • Familiarity with ML model serving, scaling and monitoring frameworks in production
  • Strong communications skills to convey technical decisions and engage with clients effectively

Responsibilities

  • Build and maintain platform components for ML model training, deployment, serving and monitoring
  • Develop and optimize CI/CD pipelines for machine learning workflows
  • Implement and support model lifecycle management, including registries and observability tooling
  • Design and manage scalable, secure deployments using containerization and Kubernetes
  • Enable secure, reusable and automated workflows to enhance ML developer productivity
  • Extend platform capabilities to support LLMOps, RAG and agentic AI workloads
  • Collaborate with engineering teams to improve reliability, automation and operational maturity
  • Apply governance and compliance standards across AI operations
  • Participate in presales and client-facing sessions to translate requirements into scalable solutions
  • Advocate cloud best practices for reliability, scalability and cost optimization

Skills

AirflowAWSAzureDockerGCPKubernetesOpenTelemetryPythonTerraform

Certifications

AWSAzureGCPCKACKAD

Degrees

Bachelor’s or Master’s degree in Computer ScienceEngineering or related discipline

Relocation

No