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.
Job Purpose
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.
Job Duties
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, including costs and failures. Honest negative findings are valued here.
What we're looking for
Strong professional software engineering experience, including building and operating production software, and the ability to read and critically assess code outside your own main language.
Excellent programming and software design skills, and a strong grasp of testing, CI/CD, code review and modern development practice.
Hands-on experience building applications with LLM APIs, including tool calling and agentic workflows.
A practical understanding of how LLMs and coding agents fail: plausible but wrong code, fixes that mask symptoms, tests gamed to pass, context limits, and cost growth.
Day-to-day experience using AI coding tools and agents on real codebases, with an evidence-based view of where they help and where they hurt.
Experience integrating with developer systems such as Git platforms, CI/CD, issue trackers and observability tooling.
Experience designing evaluations for AI outputs, or comparable experimental rigour.
Comfort preparing code, documents and tickets for retrieval using Python and SQL, and working with a data team on anything larger.
Credibility with engineers, and the ability to influence without line authority.
A pragmatic, experimental mindset, including willingness to stop your own ideas when the evidence says so.
Tools and Technologies
You will not need every tool below. Equivalent experience with comparable tools is equally welcome:
- Languages: Python or a comparable language for tooling and AI services, and comfort working across languages and codebases.
- LLM and agent tooling: LLM APIs and open-weight models (provider-hosted or self-hosted), tool/function calling, Model Context Protocol, agent frameworks, and AI coding tools and agents.
- Engineering platform: Bitbucket, Jenkins (moving to Harness.io), Docker, Kubernetes, AWS , FastAPI or similar for internal services, and tracing/evaluation tooling.
Particularly valuable: developer productivity or developer experience engineering, DORA-style measurement, platform engineering, RAG and vector search, deploying and evaluating self-hosted open-weight models, or experience in a regulated or customer-facing environment such as gambling or financial services.
Not required: training models from scratch or research experience. This is an AI engineering role, not an AI research role. Experience running open-weight models yourself, fine-tuning, or working with embeddings is a plus.
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