Senior AI Engineer

BALLY’S INTRALOT SA; Deutschland; On-site; EUR 90,000 - 140,000; Full time

Job summary

Bally's Intralot SA is expanding its AI Transformation team to build new capabilities, tools, and automations that accelerate delivery and improve quality. You will pair with Product and Technology to translate ideas into practical AI-powered products and services.

The role focuses on prototyping, experimentation, and productionisation of AI solutions, with a strong emphasis on production readiness, security, and measurable outcomes across our global platforms.

Qualifications

• Strong Python or TypeScript skills with production experience.
• Experience building LLM applications, agents or automations; proficient with AI coding tools.

Responsibilities

• Originate and build new AI capabilities across Product and Technology, turning ideas into usable products and tools.
• Run experiments with clear hypotheses, success metrics, and controlled rollouts to inform decisions.
• Take prototypes into production and address architecture, security, testing and release readiness.

Skills

Python

TypeScript

AI engineering

APIs

Cloud deployment

Tools

AWS

CI/CD

Observability

Job description

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.

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 analysi