Junior DevOps / MLOps Engineer

Adria Solutions; Netherton; Hybrid; GBP 25,000 - 38,000; Full time

Benefits offered by this job

Hybrid / Remote working

Flexible working

Other benefits

Job summary

Adria Solutions is seeking a Junior DevOps / MLOps Engineer to help build, automate and maintain the infrastructure and pipelines that deliver software and machine learning models into production. You will work alongside software and AI engineers to support reliable deployments.

This early-career role requires a foundation in cloud computing, scripting or software development and an interest in ML infrastructure, with opportunities to learn GitHub, AWS, and CI/CD practices.

Qualifications

• Experience with version control using Git or GitHub.
• Basic knowledge of AWS services and cloud infrastructure.
• Scripting or programming experience (Python, Bash or PowerShell).
• Understanding of automated testing and deployment principles.
• Strong problem-solving abilities and willingness to learn.
• Interest in how ML models are deployed, monitored and maintained.

Responsibilities

• Assist configuring and maintaining AWS infrastructure for apps and ML workloads.
• Help build and maintain CI/CD pipelines using GitHub.
• Automate routine deployment and operational tasks.
• Support packaging, versioning and deployment of ML models.
• Monitor infrastructure and pipelines and help implement fixes.
• Collaborate with developers to keep dev/test/prod environments consistent.
• Follow access management, secrets handling, security and documentation best practices.
• Use GitHub Copilot to assist development, reviewing generated code.

Skills

Git & GitHub

AWS basics

Python scripting

Bash/PowerShell

CI/CD concepts

Testing & deployment

Problem solving

ML deployment interest

Tools

GitHub Actions

Docker

Terraform / CloudFormation

Job description

Junior DevOps / MLOps Engineer

We are looking for a Junior DevOps / MLOps Engineer to help build, automate and maintain the infrastructure and pipelines that deliver software and machine learning models into production.

Working alongside software and AI engineers, you will support reliable deployments, improve automation and help ensure production systems operate effectively as demand grows. This role would suit an early-career engineer with a foundation in cloud computing, scripting or software development and an interest in machine learning infrastructure.

Key Responsibilities

• Support the configuration and maintenance of AWS infrastructure for software applications and machine learning workloads.
• Help build and maintain CI/CD pipelines using GitHub and associated automation tools.
• Automate routine deployment, configuration and operational tasks.
• Support the packaging, versioning and deployment of machine learning models.
• Monitor infrastructure and deployment pipelines, investigate issues and help implement fixes.
• Work with developers to maintain consistent development, testing and production environments.
• Follow good practices for access management, secrets, security and documentation.
• Use GitHub Copilot to support development and automation, reviewing and testing generated code carefully.

Essential Skills and Experience

• Practical experience gained through employment, an apprenticeship, university projects or personal development.
• Familiarity with Git and GitHub for version control and collaborative development.
• A basic understanding of AWS services and cloud infrastructure.
• Some scripting or programming experience, ideally Python, Bash or PowerShell.
• An understanding of automated testing and software deployment principles.
• Strong problem-solving skills and a willingness to learn.
• An interest in how machine learning models are deployed, monitored and maintained.

Desirable Experience

• GitHub Actions or another CI/CD tool.
• Linux administration and Docker containers.
• Infrastructure as code, such as Terraform or AWS CloudFormation.
• Exposure to model versioning, experiment tracking or MLOps tools.
• Experience using GitHub Copilot.

What You Will Contribute

You will help create repeatable deployment processes, reduce manual work and improve the reliability of software and machine learning systems in production.

Benefits

• Hybrid / Remote working
• Flexible working
• + other benefits