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Hire an AI/ML Platform Engineer

AI/ML Platform Engineers help businesses build the infrastructure and internal platforms needed to develop, deploy, and manage machine learning systems at scale. As organisations invest more heavily in AI capability, demand is growing for engineers who can create stable, scalable environments that support faster model development, deployment, and operational performance.

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What What does an AI/ML Platform Engineer do?

An AI/ML Platform Engineer develops and manages the infrastructure, tooling, and internal platforms that support machine learning development and deployment. Their role focuses on improving scalability, automation, reliability, and operational efficiency across AI environments.

AI/ML Platform Engineers commonly work on:

Machine learning deployment platforms
MLOps and automation workflows
Model monitoring and lifecycle management
Cloud-native AI infrastructure
CI/CD pipelines for AI systems
Internal tooling for AI and engineering teams

They often work closely with machine learning engineers, platform teams, DevOps specialists, and software developers to support production-ready AI systems.

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Need to hire an AI/ML Platform Engineer?

As AI systems grow more complex, many businesses struggle with deployment, scalability, and operational management. AI/ML Platform Engineers help create the infrastructure and tooling needed to support machine learning environments more effectively.

AI/ML Platform Engineers develop infrastructure that allows AI teams to build, deploy, and manage models more efficiently at scale.

Strong candidates help reduce manual processes through CI/CD pipelines, automated deployment workflows, and model lifecycle management.

Most AI/ML Platform Engineers work extensively with AWS, Azure, or Google Cloud alongside containerisation and orchestration technologies.

AI systems require monitoring, optimisation, and long-term infrastructure management. Platform Engineers help improve stability and reduce operational bottlenecks.

Well-designed ML platforms allow data science and AI teams to experiment, deploy, and iterate more quickly across production environments.

Hiring for AI/ML Platform Engineer roles? Ask about…

David Berwick, Adria Solutions

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MLOps Pipelines Kubernetes and Docker CI/CD for ML Cloud Platforms (AWS, Azure, GCP) Model Monitoring and Retraining Infrastructure as Code MLflow or Kubeflow Data Pipeline Integration API Deployment Distributed Systems SEE LIVE JOBS

Businesses typically hire AI/ML Platform Engineers when machine learning projects begin scaling and operational complexity increases.

You may need to hire an AI/ML Platform Engineer if you are:

Expanding internal AI or machine learning capability
Building production AI environments
Improving MLOps and deployment workflows
Managing multiple machine learning models at scale
Developing internal AI platforms or tooling
Scaling cloud-based AI infrastructure

Many organisations also hire AI/ML Platform Engineers during AI transformation projects or when data science teams begin moving models into production more regularly.

Strong AI/ML Platform Engineers combine infrastructure expertise with deep understanding of machine learning operations and cloud-native engineering. The best candidates can improve scalability and automation while supporting fast-moving AI environments.

A strong hire will typically have:

Experience with cloud platforms such as AWS, Azure, or GCP
Knowledge of Kubernetes, Docker, and containerised systems
Experience building CI/CD pipelines for AI environments
Understanding of MLOps and model lifecycle management
Strong automation and infrastructure-as-code capability
Experience supporting scalable machine learning systems
Strong systems thinking and operational problem-solving skills

The most in-demand candidates are often those with experience building internal ML platforms that support large-scale AI deployment and experimentation.

Why hiring AI/ML Platform Engineers is critical and complex

This role requires deep knowledge of infrastructure, DevOps and machine learning workflows. Many engineers are experienced in software delivery or cloud, but fewer understand the unique demands of AI systems at scale. Finding someone who can build tools that empower entire ML teams takes a targeted search.

At Adria Solutions, we understand the demands of modern AI infrastructure. We help businesses hire Platform Engineers who turn scattered workflows into unified, scalable systems that accelerate machine learning delivery.

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AI/ML Platform Engineer salary expectations

AI/ML Platform Engineer salaries continue to rise as businesses invest more heavily in scalable AI infrastructure and machine learning operations. In the UK, most permanent salaries range from £75,000 to £135,000+, while contract rates commonly fall between £700 and £1,150+ per day.

Higher salaries are typically associated with candidates who have experience building cloud-native ML platforms, supporting large-scale deployment environments, and improving automation across AI systems. Demand remains particularly strong across SaaS, fintech, enterprise AI, and cloud technology businesses.

Level	UK Salary Range	Contract Day Rate
Mid-level	£75,000 – £100,000	£700 – £900
Senior	£100,000 – £135,000+	£900 – £1,150+

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FAQs

Got questions? Find quick answers to the most common queries here.

An AI/ML Platform Engineer builds and manages the infrastructure, tooling, and deployment platforms that support machine learning systems in production environments.

An AI/ML Platform Engineer focuses more broadly on platform infrastructure, scalability, and internal tooling, while an MLOps Engineer is typically more focused on deployment workflows, automation, and model operations.

Many AI/ML Platform Engineers work with Kubernetes, Docker, Terraform, AWS, Azure, Google Cloud, CI/CD pipelines, and machine learning platform tooling.

Without strong platform infrastructure, machine learning projects can become difficult to scale, deploy, monitor, and maintain effectively across production environments.

Businesses should look for candidates with strong cloud infrastructure expertise, MLOps knowledge, automation capability, and experience supporting scalable machine learning systems in production.

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