MLOps Engineer: Production ML Pipelines & Observability

CoreWeave; Greater London; On-site; GBP 100,000 - 150,000; Full time

Benefits offered by this job

Family-level Medical Insurance

Family-level Dental Insurance

Generous Pension Contribution

Life Assurance at 4x Salary

Critical Illness Cover

Employee Assistance Programme

Tuition Reimbursement

Job summary

CoreWeave is seeking an experienced MLOps Engineer to own the end-to-end ML lifecycle—experimentation, training, packaging, deployment, serving, and retirement. You will define MLOps practices, establish SLOs/SLAs, and build automated CI/CD and continuous training pipelines for production readiness.

You will implement observability, data/versioning, and drift monitoring while ensuring robust security and governance.

Qualifications

• 5–6+ years of professional experience in MLOps, ML platform engineering, ML infrastructure, or SRE/DevOps for production machine learning systems.
• Deep hands-on experience implementing observability for ML systems, including monitoring inference availability, latency, throughput, GPU/resource utilization, and data or model drift.
• Proficient in Python for platform tooling, infrastructure integration, and pipeline automation, with strong infrastructure-as-code and CI/CD practices.

Responsibilities

• Own the end-to-end MLOps surface across experimentation, training, packaging, deployment, serving, and retirement.
• Define and roll out MLOps practices, SLOs/SLAs, and build automated CI/CD and continuous training pipelines.
• Implement model observability, data versioning, and drift monitoring with security and governance controls.
• Collaborate with product, data science, and core infrastructure to optimize GPU compute utilization and incident response.

Skills

MLOps

ML platform engineering

ML infrastructure

SRE/DevOps

Python

Kubernetes

Cloud platforms

CI/CD

Observability

Reliability engineering

Tools

Kubernetes

Python

CI/CD

Terraform

MLFlow

Job description

CoreWeave is seeking an experienced MLOps Engineer to own the end-to-end ML lifecycle—experimentation, training, packaging, deployment, serving, and retirement. You will define MLOps practices, establish SLOs/SLAs, and build automated CI/CD and continuous training pipelines for production readiness.

You will implement observability, data/versioning, and drift monitoring while ensuring robust security and governance.