Senior AI Engineer

About Us

Our mission is to dominate the betting and gaming industry on a global scale and we need the very best Tech talent to help us achieve this.

With our proprietary platform powering everything we do, it's an exciting time to join us. We're pioneering new products and driving more advanced, creative technologies. The result? Unrivalled experiences for millions of customers worldwide.

Betfred's Technology department is driven by innovation, and you'll be at the heart of unlocking our platform's potential. So, if you want to help shape the future of betting and gaming, it's time to join us.

We have around 250 engineers building and running platforms for a leading digital and retail business in the global betting and gaming industry. We want to use LLMs and coding agents to help them deliver better software, faster, with less wasted effort, and we want to be able to prove that it works.

This is a hands-on engineering role. You will work out where AI genuinely helps our engineers and where it does not, build the tooling and workflows that make it work in our real codebases, and measure the results honestly. You will use the best available models, coding agents and open-source tools. We are not looking to build models.

There is no settled playbook for this. The tools change every few months and the evidence on what works is mixed, so you will be making judgement calls, documenting them, and revisiting them as the evidence arrives.

You will work with our engineering teams, not around them. You will have the backing of the Engineering Director and the CIO, a budget for tools, and the freedom to run experiments, with the expectation that you stop the ones that do not work and report the results plainly, whatever they show.

You will not be doing this alone. An external AI adviser with deep applied LLM experience takes part in the selection process and will support you after you join, as a sounding board on tooling, methodology and approach. You own the work and the recommendations. The adviser challenges your thinking and brings outside experience.

Map how work flows through our engineering lifecycle, from idea to production, and find where effort, delay and rework actually concentrate. Writing code is often not the constraint.

Establish baselines before changing anything, working with engineering, delivery and product teams: cycle time, review time, deployment frequency, change failure rate, time to restore, and developer feedback.

Review the AI tools and initiatives already in use or under way across engineering, assess each against evidence of value and cost, and drive forward those that deliver. Close those that do not, or that can be solved better by other means such as conventional automation, a process change or an existing tool.

Design and run pilots, with comparison groups where practical, covering AI coding assistants, coding agents, automated code review, test generation, bug triage and fixing, migrations, documentation and onboarding.

Build and maintain the integrations that make AI useful in our environment: repository context and instruction files, CI/CD hooks, review assistants, agent workflows that open draft pull requests, and access to issue trackers, logs and documentation through APIs or MCP.

Own the evaluation harness for everything you build: test sets, regression checks, tracing and cost monitoring, so we know how often agents are right, what they cost, where they fail, and whether fixed bugs stay fixed.

Define how engineers use AI safely and well: pull request size limits, test requirements, accountability for AI-assisted code, and what code and data may go to which tools.

Work with Security, Legal and Data Protection on vendor, IP, privacy and code-security controls.

Evaluate hosted and self-hosted model options against our requirements for code and data security, and recommend what we should run where. Our code may have to stay inside our own environment, so you will work within real constraints.

Coach engineers and win adoption, especially from sceptics, while protecting the development of junior engineers.

Choose the right tool for each problem: an LLM, an agent, conventional automation (linters, codemods, better CI) or no change at all, and decide when to build and when to buy.

Build the business case: quantify the costs and benefits of what you build, show the return on investment to engineering and executive leadership, and recommend what to scale, change or stop.

Report results openly,…