AI Jobs in Cloud & Infrastructure UK
Salary, Roles & Top Employers
Cloud and infrastructure is the backbone of every AI system in production. AWS, Google Cloud, Azure, Databricks, and Snowflake UK all maintain substantial AI and data engineering teams. This guide covers the roles, skills, and salaries in the sector.
What AI Looks Like in UK Cloud & Infrastructure
Cloud and infrastructure companies sit at the intersection of two major trends: the explosive growth of AI workloads and the maturation of cloud-native infrastructure tooling. AWS, Google Cloud, and Azure aren't just providing the compute on which other companies' AI runs — they're building their own substantial AI product and engineering teams, from ML services that are sold to customers to internal systems that run the platforms themselves.
For UK-based AI and data engineers, cloud companies offer some of the most technically demanding and well-compensated work in the sector. The scale at which these teams operate is unmatched — training pipelines that process petabytes of data, inference systems serving billions of predictions per day, and data platforms handling millions of concurrent queries. The engineering problems are genuinely hard.
Beyond the hyperscalers, the data infrastructure layer — Databricks, Snowflake, Cloudflare — has become a significant employer. Databricks UK in particular has been one of the most active hirers of ML and data engineers in the UK since 2023, building out its engineering presence in London alongside its commercial operations. These companies offer the combination of hyperscaler-grade technical problems with slightly more startup-like pace and equity upside.
Top UK Cloud & Infrastructure Employers
Amazon Web Services (AWS)
Hyperscaler
SageMaker engineering, Alexa AI in Cambridge, and large-scale data infrastructure teams
Google Cloud UK
Hyperscaler
Vertex AI platform engineering, BigQuery ML teams, and DeepMind infrastructure
Microsoft Azure UK
Hyperscaler
Azure ML, Azure OpenAI Service engineering, and Microsoft Research Cambridge
Databricks UK
Data & AI platform
MLflow creators; Delta Lake, Spark, and ML platform engineering in London
Snowflake UK
Data cloud
Snowpark ML, data engineering, and Snowflake ML feature store teams
Cloudflare London
Cloud networking
AI Workers platform, edge inference, and distributed systems engineering
Key AI Roles in UK Cloud & Infrastructure
MLOps Engineer
Building and maintaining ML training pipelines, model registries, serving infrastructure, and monitoring systems at scale. The highest-volume AI title in cloud companies.
Data Engineer
Building the data pipelines, lakes, and warehouses that feed ML systems. SQL, Spark, dbt, and Kafka are the primary tools. Frequently works alongside ML engineers.
AI Infrastructure Engineer
Designing and building the compute infrastructure for AI training — GPU cluster management, distributed training systems, and inference optimisation at scale.
Platform Engineer (ML)
Building the internal developer platforms that allow ML teams to train, deploy, and monitor models without infrastructure friction. Kubernetes and cloud-native tooling.
ML Engineer (Cloud Services)
Building ML features within cloud products — SageMaker, Vertex AI, Azure ML. Customer-facing ML engineering at the product layer, not just internal infrastructure.
AI Salary Ranges in UK Cloud & Infrastructure (2026)
Hyperscalers (AWS, Google, Microsoft) pay at the top of these ranges. Data infrastructure companies (Databricks, Snowflake) are competitive with additional equity. Ranges are base salary only.
| Role | London | Rest of UK / Remote |
|---|---|---|
| Data Engineer (mid) | £55,000 – £85,000 | £45,000 – £72,000 |
| MLOps Engineer (mid) | £65,000 – £105,000 | £55,000 – £88,000 |
| Senior MLOps / Platform Engineer | £105,000 – £155,000 | £85,000 – £130,000 |
| AI Infrastructure Engineer (mid) | £75,000 – £115,000 | £62,000 – £95,000 |
| Staff / Principal Engineer | £155,000 – £220,000+ | £120,000 – £185,000+ |
RSU grants at AWS, Google, and Microsoft add significantly to total compensation at senior levels — commonly £40,000–£120,000+ annually in stock at Staff+ levels.
In-Demand Skills
Kubernetes
Non-negotiable for MLOps and platform roles. GPU scheduling, cluster autoscaling, and persistent volumes for ML workloads.
Apache Spark / PySpark
Large-scale data processing. Core at Databricks and expected at any company processing ML training data at scale.
Python
Primary language for ML workflows, pipeline orchestration, and infrastructure automation.
Terraform / Infrastructure as Code
Provisioning cloud ML infrastructure reproducibly. Expected for any senior platform or infrastructure role.
Apache Kafka
Real-time data streaming for feature pipelines and ML inference event streams. High demand at Databricks, AWS, and Cloudflare.
MLflow / Weights & Biases
Experiment tracking and model lifecycle management. MLflow was created by Databricks and is widely deployed.
dbt (data build tool)
Transformations and analytics engineering. High demand at Databricks and Snowflake, where dbt is part of the core data stack.
Ray / Dask (distributed compute)
Distributed Python frameworks for scaling ML workloads. Ray is increasingly common at AI-native cloud teams.
Career Entry Routes
From DevOps or software engineering
The most common transition. DevOps and SRE engineers who develop ML knowledge — understanding model training workflows, data pipeline patterns, and ML monitoring — are well-positioned for MLOps roles at cloud companies. Kubernetes experience is particularly transferable.
From data engineering
Data engineers who upskill in ML system design (feature stores, model serving, training pipelines) move naturally into MLOps. The underlying skills — distributed data processing, pipeline orchestration, cloud platforms — overlap significantly.
Cloud provider graduate programmes
AWS, Google Cloud, and Microsoft UK all run engineering graduate programmes that rotate through infrastructure and ML teams. These are highly competitive but provide direct entry into hyperscaler engineering teams with structured mentorship.
Databricks and Snowflake hiring from adjacent tech
Databricks UK and Snowflake UK both actively recruit experienced engineers from adjacent tech companies. A background in distributed systems, database engineering, or ML platform work — combined with familiarity with their respective products — is the strongest application profile.
Frequently Asked Questions
Sub-Sector Quick Facts
415+ live roles
£45,000 – £220,000+
London, Cambridge, Edinburgh
Common; many roles remote-first