Junior DevOps / MLOps Engineer (Hybrid/Remote)

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

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.