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Machine Learning Engineer · United Kingdom

Lead AI Engineer

intralot·Manchester, Manchester, United Kingdom

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Bally’s Intralot is a newly formed company established through the combination of Intralot and Bally’s International Interactive, positioning the group as a top iGaming operator and a leading global provider of lottery solutions.

Bally’s Intralot is uniquely positioned across digital online gaming markets, lottery, iLottery, and sports betting. Operating at the intersection of technology, regulation, and entertainment. We are optimally positioned to capitalise on strong global growth trends driven by digital adoption and regulatory expansion across lottery and iGaming markets.

Well, what about the team?

You'll lead a new AI Engineering function that runs in two halves under one team: R&D proves what's possible in personalisation, recommendation, search and agents; a delivery layer ships it into the live product for real players.

This is a founding leadership role. You'll build density around an established AI R&D nucleus, drive its technical execution, grow the engineers in it, and stay close enough to the work to lead from the front across the lobby and discovery experience and the content pipelines behind it: recommendation, contextual bandits, dynamic categorisation, advanced search, agents, and automatic content generation.

It's a hands-on leadership role: you own the technical bar and the how, line-manage and grow the team, and carry enough technical weight to make the hard calls and be trusted on them.

So, what will you be doing?

  • Drive the technical execution of the AI R&D agenda: decide how problems get approached, which technical and architectural bets are worth making, and how capability gets proven across personalisation, recommendation, search and agents.
  • Lead one team spanning R&D and delivery: line-manage, grow, and unblock the engineers, and be accountable for both proving capability and shipping it into the live product.
  • Set the production bar and standards for AI features end to end — how experimental work crosses into hardened, observable, maintainable systems without losing its edge.
  • Stay hands-on where it counts: lead from the front on the hard problems, pressure test the technical bets, and be credible in the code without owning delivery of every feature yourself.
  • Own the path from validated prototype to live: real-time personalisation, recommendation, search and ranking against live traffic, within real latency budgets and reliability realities.
  • Hold a high engineering bar on AWS and TypeScript — clean CI/CD, infrastructure as code, observability, testing, and LLMOps for running model-backed systems in production.
  • Shape delivery against the roadmap with the VP, product managers, data analysts and Staff Engineers, and make the capability compound across brands rather than be rebuilt per brand.
  • Work within Responsible Gambling and UKGC requirements, in a regulated, real money, real-time domain.
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