Senior MLOps Engineer

Benefits

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Mental health support

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Work mode

Remote From the posting source checked Jun 20, 2026

In-office days

0 days From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Senior MLOps Engineer Remote, UK Senior MLOps Engineer The Role As a Senior MLOps Engineer, you will be the backbone of our AI production lifecycle. You will bridge the gap between research and real-world application, ensuring our Data Scientist, AI Researchers, Product teams and others in the company have the high-performance infrastructure, automated pipelines, and deployment strategies needed to ship state-of-the-art models and agents at scale. Who We're Looking For 5+ years experience with cloud infrastructure and infrastructure as code. Previous experience with the ML and LLM lifecycle - training, hosting, optimisation, observability. Used to working closely with researchers and data scientists - taking experiments from worksheets into production. Strong grasp of ML fundamentals and modern GenAI stack. What You'll be Doing Infrastructure & Platform Engineering Infrastructure as Code (IaC): Design and maintain scalable cloud environments (GCP/AWS) using Terraform. Resource Provisioning: Manage GPU/TPU resource allocation for training, fine-tuning, and interactive notebooks. Custom Tooling: Build internal services and CLI tools to streamline the developer experience for the AI team. ML & LLM Orchestration & Observability Automated Pipelines: Design CI/CD and training pipelines using tools such as GitHub Actions, MLFlow, Vertex AI Pipelines. Ensure high quality training data (e.g. introducing a feature store). Deployment Methodology: Develop reusable patterns for model serving. Managing service deployments to Kubernetes. Vector Infrastructure: Manage and optimize vector databases and embedding pipelines for RAG-based systems. Observability and Reliability: Model drift monitoring, resource utilisation, LLM and agent tracing. Performance & Optimization Inference Optimization: Implement techniques to reduce latency and

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