Senior AI Engineer

The Role

We re looking for a Senior AI Engineer to take Stakemate s use of AI from experiments to a real, production-grade capability. Reporting to the CTO, you ll work with the business to find high-value AI opportunities, shape the platform and architecture, and take agents from a working POC to production - then set the governance and standards that keep all of it safe as we scale.

This is a role for someone who s done the hands-on building already and can help set the direction for how a whole company uses AI.

• You set the bar: from the first POC to the platform everyone else builds on.
• Direct senior leadership access: you ll help decide where AI goes next at Stakemate, not just work a backlog someone else wrote.
• Real production stakes: your agents run in a live betting product, not a slide deck.
• Scaleup energy: fast decisions with a relentless focus on the player experience

Founded in summer 2022, Stakemate is revolutionising sports betting by putting social features and seamless interfaces at the forefront. We re a profitable startup that s grown over 15x in the last year, and we re just getting started.

Recognised by EGR as one of the most innovative startups in gaming, we re scaling fast and shaping the next generation of sports entertainment. We ve built a product that users genuinely love — with unlimited betting group chats, multi-game bet builders, and an experience designed for how people actually want to bet: together.

Revolut disrupted Barclays, Robinhood disrupted Etrade — Stakemate is executing in the same way to take on the traditional gaming sector.

Learn more at  or download our app to see what we re building.

• AI-first by default: you reach for an agent or an automation before you reach for a hire or a manual process.
• Hands-on: you d rather build the thing than write a strategy document about building the thing.
• Direct: you say when something is a bad idea, including when the bad idea is yours
• Comfortable being the expert: you can be the person everyone asks without becoming the bottleneck or the single point of knowledge.

• Demonstrable impact from agentic or LLM-powered systems you ve shipped to real users, and you can explain what broke and what you changed.
• Hands-on with an agent framework (Lang Graph, Llama Index, Semantic Kernel, ADK or similar) and RAG in production: embedding models, vector stores, re-ranking, and knowing when a live query beats retrieval.
• Strong Python experience or similar, on a real engineering foundation: testing, version control, CI/CD, and the APIs that serve your own work.
• Hands-on with a major cloud and its managed AI services (Azure and AI Foundry, or the GCP/AWS equivalents), plus solid SQL and relational modelling.
• Architectural judgement: you make the design call, defend the trade-offs, and know where an LLM system needs optimising on cost, latency, and output that only sounds right.
• Strong product sense: you re data-driven, you understand what players actually need, and you think through the second and third order effects before you ship.

• Proving an AI system behaves rather than trusting it: eval sets, output scoring, tracing, regression gates.
• Retrieval pipelines at volume: embedding at scale, index freshness, accuracy as the underlying data moves.
• Experience with workflows that survive contact with reality: timeouts, failed APIs, a human approving step.

Be curious:

• Sit with the teams who feel the pain, find where an agent would genuinely pay for itself, and get something in front of them quickly enough to learn whether you were right.
• Choose the approach and own the reasoning: low-code, pro-code, or something bought off the shelf, weighed on cost, control and how fast it can land.

Get…