How to Become an MLOps Engineer
in the UK: A Complete 2026 Guide

MLOps engineering is one of the most in-demand specialisms in UK tech, sitting at the intersection of software engineering, infrastructure, and machine learning. There are two well-worn paths in — and this guide covers both in detail, along with the complete learning roadmap and the portfolio project that actually gets you hired.

The Two Main Entry Paths

Path 1: From DevOps or SRE

This is the faster path to an MLOps role. If you're a DevOps engineer or SRE, your Kubernetes, CI/CD, and infrastructure-as-code skills are directly transferable — these are foundational in MLOps. The specific gaps you need to close:

• Experiment tracking — understanding why ML experiments need to be logged (parameters, metrics, artefacts) and how to use tools like MLflow or Weights & Biases
• Data versioning — understanding why data is an artefact alongside code, and how DVC or similar tools manage this
• Model monitoring — knowing what data drift and concept drift mean and how to detect them in production using tools like Evidently AI
• ML pipeline awareness — enough understanding of how models are trained, validated, and evaluated that you can design infrastructure around the process

The transition from SRE to MLOps typically takes 3–6 months of focused learning and one strong portfolio project demonstrating the ML-specific skills.

Path 2: From ML Engineering or Data Science

If you're coming from an ML or data science background, you have deep domain knowledge but likely gaps in the infrastructure and operational side. The skills to build:

• Containerisation and orchestration — Docker and Kubernetes are non-negotiable. Get to a level where you can write production Kubernetes manifests and Helm charts without looking up every parameter.
• CI/CD for ML — extend your software CI/CD knowledge to include model training triggers, evaluation gates, and automated deployment pipelines.
• Infrastructure as code — Terraform or Pulumi for provisioning the compute and storage that ML systems need.
• Observability — Prometheus, Grafana, and alerting for both infrastructure and model performance metrics.

The Full Learning Roadmap

In rough order of priority:

1. Containerisation — Docker fluency (writing Dockerfiles, multi-stage builds, optimising image sizes) before anything else
2. Kubernetes — deployments, services, ingress, persistent volumes, RBAC. Practise on a local cluster with Minikube or K3s
3. CI/CD — GitHub Actions or GitLab CI to automate model testing and deployment
4. MLflow — set up experiment tracking, model registry, and understand the model lifecycle
5. DVC — data versioning and pipeline management alongside Git
6. Model serving — learn at least one serving solution: FastAPI for simple APIs, then a dedicated serving platform
7. Model monitoring — set up drift detection and alerting with Evidently AI
8. IaC — Terraform basics, enough to provision cloud resources for an ML system

The CKA Certification — Is It Worth It?

The Certified Kubernetes Administrator (CKA) certification appears in many MLOps job descriptions. Our view: it's worth pursuing if you don't already have deep Kubernetes experience from a prior role. The exam is hands-on (you're working in a live cluster, not answering multiple choice) and the preparation process builds genuine operational Kubernetes knowledge. If you already have 2+ years of Kubernetes work behind you, your time is better spent on ML-specific tooling.

The Portfolio Project That Gets You Hired

Build one end-to-end ML system and document it thoroughly. The project should demonstrate:

• A model that trains on a dataset (not a Jupyter notebook — a proper Python package)
• Containerised in Docker and deployable to Kubernetes
• A CI/CD pipeline that runs tests and retrains on schedule or on data changes
• Experiment tracking via MLflow — all runs logged, model versions managed
• A serving endpoint with a health check and basic authentication
• Monitoring that detects when input data distribution shifts and fires an alert

The dataset and model don't matter much — a simple tabular classification problem works perfectly. What matters is the infrastructure and operational rigour around it. Put it on GitHub with a clear README, architecture diagram, and documented design decisions.

What the First 90 Days Actually Look Like

Most new MLOps engineers spend the first month understanding the existing system — how training pipelines are triggered, where models are stored, how deployments work, what monitoring exists. The second month typically involves improving something: faster training runs, more reliable deployments, better monitoring coverage. By 90 days, you should own at least one component of the ML platform end-to-end.

Frequently Asked Questions

Do I need ML skills to become an MLOps engineer?

You need enough ML understanding to work effectively with data scientists — knowing what training pipelines do, what evaluation means, why data quality matters. You don't need to train models from scratch.

How is MLOps different from DevOps?

DevOps handles deterministic software; MLOps handles probabilistic systems that degrade gradually rather than failing cleanly. Data is as much an artefact as code, and reproducibility requires tracking experiments, data versions, and model artefacts alongside source code.

Is the CKA certification necessary?

Not strictly, but it demonstrates Kubernetes depth that many job descriptions require. Worth pursuing if you don't already have deep Kubernetes experience from prior work.

What's the fastest path from SRE?

Your Kubernetes and infrastructure skills are strong. Close the gaps: experiment tracking, data versioning, model monitoring. Build the end-to-end portfolio project. This transition typically takes 3–6 months with focused effort.

What portfolio project should I build?

A complete end-to-end ML system: train → containerise → deploy to Kubernetes → CI/CD → experiment tracking → monitored endpoint. One well-documented project demonstrates every core MLOps skill.