Hybrid Production ML Engineer — AI Model Maturation & Edge

Zaizi Limited; Cheltenham; On-site; GBP 75,000 - 110,000; Full time

Benefits offered by this job

Competitive pay

Loyalty pension (starting at 5%, up to

Life Assurance

Job summary

Zaizi Limited is seeking a Machine Learning Implementation Engineer to take AI models from research into robust, production-ready software in a permanent role based in Cheltenham. You will drive TRL advancement, ensure scalable, reliable solutions, and integrate ML into business workflows for automation and insight.

You’ll refine model code, mentor juniors, and tackle complex technical hurdles from prototype to product while staying current with AI implementation best practices in a

Qualifications

• Broad technical expertise in machine learning and software engineering best practices.
• Proven experience turning research-grade AI/ML code into production software.
• Ability to evaluate new generative AI technologies for practical deployment in the National Security Domain.
• Experience delivering proofs of concept bridging research and application.
• Data science techniques to support model refinement and hardening.
• Experience deploying ML models into live environments.

Responsibilities

• Refine model code for performance and scalability.
• Mentor junior team members and influence technical decisions.
• Design and manage ML model integration into broader software architectures.
• Adapt models for edge hardware and Android-based devices.
• Stay current with AI implementation best practices.

Skills

ML algorithms

Software engineering

Production software

Edge deployment

Data science

Team mentorship

Job description

Zaizi Limited is seeking a Machine Learning Implementation Engineer to take AI models from research into robust, production-ready software in a permanent role based in Cheltenham. You will drive TRL advancement, ensure scalable, reliable solutions, and integrate ML into business workflows for automation and insight.

You’ll refine model code, mentor juniors, and tackle complex technical hurdles from prototype to product while staying current with AI implementation best practices in a