Senior AI Engineer

Bally's Intralot; Manchester; On-site; GBP 90,000 - 120,000; Full time

Benefits offered by this job

Health insurance

Home office allowance

Wellness or Gym allowance

Flexi-block policy for international/4

Annual Summer and Christmas parties

Job summary

Bally's Intralot is seeking a Senior AI Engineer to join our AI Transformation team in the United Kingdom. You will own ideas from hypothesis to production, delivering AI-enabled capabilities and shared tooling across Product and Technology.

You will turn prototypes into reliable products, run controlled experiments, and help teams adopt practical AI engineering patterns. You will mentor colleagues, showcase work through demos, and collaborate with cross-functional teams to balance quality, cost

Qualifications

• Strong Python or TypeScript skills and experience taking prototypes into production, including APIs, cloud deployment (AWS), automated testing, CI/CD and observability.
• Experience building LLM applications, agents or automations, using AI coding tools effectively, and improving results through evals and error analysis.

Responsibilities

• Originate and build new capabilities across Product and Technology, turning ideas into practical proposals and products.
• Run experiments with clearly defined hypotheses and success measures, using feature flags and controlled rollouts where appropriate.
• Take prototypes into production, resolving architecture, integration, testing, security and operational gaps and handover to owning teams.
• Improve how teams build software by redesigning planning, development, testing, review and delivery around AI capabilities.
• Own shared transformation tooling: develop and maintain applications, APIs, integrations and analytics on AWS (Radar and Tracker).
• Champion practical AI engineering through demos, talks, workshops and recorded guides.
• Evaluate tools and approaches, balancing quality, cost, security and portability with human oversight.
• Embed quality and responsible use through governance and observability.

Skills

Python

TypeScript

APIs

AWS

CI/CD

Observability

Automated testing

Job description

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.

And what will you need?

• Software engineering and delivery: Strong Python or TypeScript skills and experience taking prototypes into production, including APIs, cloud deployment (preferably AWS), automated testing, CI/CD and observability.
• Practical AI engineering: Experience building LLM applications, agents or automations, using AI coding tools effectively, and improving results through evals and error analysis. Familiarity with context, reusable skills and integrations through APIs or MCP.
• Initiative and product judgement: Evidence of spotting opportunities, creating useful solutions without a fully defined brief, and owning delivery from idea to outcome.
• Experimentation and evaluation: Ability to define hypotheses and success measures before building, run focused experiments against a baseline or control, and use evidence to decide what happens next.
• Sound engineering judgement: Ability to balance quality, reliability, security and cost, evaluate AI behaviour, and choose maintainable solutions with appropriate data access and human oversight.
• Technical evangelism and collaboration: Clear communication through demos, tech talks, workshops and written guides. Experience partnering with teams, mentoring engineers and helping others adopt effective practices.

Company Benefits

• Health insurance.
• Home office allowance.
• Wellness or Gym allowance.
• Flexi-block policy allowing you to work abroad for up to four weeks a year.
• Annual Summer and Christmas parties.

Interview Process

• Hiring Manager interview- 30-minute Teams call with Hiring Manager
• Team interview- 1.5-hour interview covering technical and behavioural areas
• Take-home assessment- To be completed within 48-72 hours