Location: Manchester
Salary: £25k P/A
Apprenticeship: Software Engineer

Shape the Future of AI in the Real World
Are you ready to start your journey in one of the most exciting fields in tech? We’re looking for a Junior MLOps Engineer to join a team of innovators working at the intersection of software engineering, machine learning, and open-source technologies.

If you have a passion for AI, DevOps, and problem-solving, this is your opportunity to dive into the fast-growing world of MLOps, with full support and mentoring along the way.

Responsibilities:
As part of a close-knit engineering squad, you’ll:

Collaborate with clients to understand requirements and help design practical solutions.

Build and deploy features alongside experienced MLOps engineers.

Take part in sprint planning, retrospectives, and code reviews.

Contribute to R&D projects and open-source tooling.

Learn cutting-edge tools, cloud platforms, and best practices in a highly supportive environment.

Engage with the wider MLOps community through blogs, videos, or tech explorations.

Responsibilities:

This role is ideal for someone with a passion for software engineering or data science, who is eager to specialise in MLOps. You don’t need direct experience, we’ll help you get there.

You should have:

Proficiency in Python (academic, personal, or work-based).

A genuine interest in cloud computing (AWS, GCP, or Azure).

Familiarity with DevOps basics like Git, Docker, and Linux.

A willingness to learn infrastructure-as-code and CI/CD practices.

A love of open-source software and a collaborative mindset.

Clear, confident communication skills.

Nice to have:

Exposure to ML libraries like PyTorch, TensorFlow, or scikit-learn.

Awareness of the MLOps landscape or tools (e.g., MLflow, Kubeflow, DVC).

What We Offer:
Competitive salary.

25 days holiday (plus bank holidays), rising to 30 with service.

Equity option scheme — own a piece of what you build.

Hybrid working with 3 in-office days (Mon, Wed, Thurs) for team collaboration.

Pension plan and Cycle to Work scheme.

A front-row seat to the open-source MLOps revolution.