Senior AI Engineer

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?

Join our AI Transformation team to create new capabilities and rethink how teams across Product and Technology work. Our programme aims to help teams ship faster, improve quality, reduce the effort and cost of delivery, build products that strengthen our competitive position, and develop our people’s skills. We turn promising ideas into working products and practices that teams can adopt and reuse across the business.

As a Senior AI Engineer, you will be empowered to identify opportunities, propose ideas and build new products, tools and ways of working. You will take ownership of your own initiatives, from a clear hypothesis and agreed success measures through experimentation, production delivery and learning. Alongside this, you will work with engineering teams to turn their prototypes into reliable production products, own shared transformation tooling, and showcase new approaches through demos, tech talks, workshops and pairing. This role combines the creativity to see what could be different with the engineering judgement and drive to make it happen.

So, what will you be doing?

• Originate and build new capabilities: Proactively identify opportunities across Product and Technology, turn them into practical proposals, and build products, internal tools, automations and agents that create value. Own selected initiatives from discovery through delivery, working with stakeholders to connect each idea to a clear programme outcome.

• Run experiments that lead to decisions: State the opportunity and hypothesis, then agree the success measure and threshold before building. Create the smallest useful experiment, use a feature flag or controlled rollout where appropriate, and measure against a control or baseline at an agreed review date. Use the evidence to scale, change or stop the work.

• Take prototypes into production: Deliver your own successful experiments and partner with engineering teams to productionise theirs. Resolve architecture, integration, testing, security and operational gaps, agree production-readiness criteria, and work through release and handover with the teams that will own and operate the resulting products.

• Improve how teams build software: Redesign planning, development, testing, review and delivery around the capabilities of AI. Build and trial approaches such as specification-driven development and agent-assisted workflows, then turn successful work into reusable skills, reference implementations, connectors and engineering patterns that teams can adapt.

• Own shared transformation tooling: Build and maintain the programme’s shared applications, APIs, integrations and analytics on AWS, including the Radar and Tracker and connections to engineering and collaboration systems. Keep these services reliable, maintainable and useful to activity owners and programme leaders.

• Champion practical AI engineering: Showcase emerging approaches and techniques through regular demos, tech talks, workshops, pairing and clear written guides. Share working examples, explain trade-offs and limitations, and turn successful experiments into practices that other teams can use.

• Evaluate tools and approaches: Keep up with developments in AI engineering, test them against real team needs, and recommend adoption based on evidence. Consider quality, cost, security and portability, helping teams retain flexibility as tools, models and pricing change.

• Embed quality and responsible use: Work with Platform, SRE, Cyber and governance colleagues to build evaluation, observability, access controls and appropriate human oversight into AI-enabled services and workflows. Help teams understand failure modes and make support, recovery and ownership arrangements clear.

• Develop capability in others: Mentor graduate and less experienced engineers, and work with learning and enablement colleagues to address practical skills gaps. Build teams’ confidence and ability to maintain what they adopt.