Senior AI Engineer

Stakemate

Senior AI Engineer responsible for bringing AI solutions to production and driving innovation at Stakemate, a fast-growing sports betting startup in London.

Posted 8/1/2026full-timeLondon • 🇬🇧 United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI-driven automation and agent frameworks, with a strong foundation in Python and cloud services. Capable of making architectural decisions, optimizing systems for performance, and ensuring robust data handling and user experience.

Highest-signal resume keywords

AI-First ApproachHands-On Experience with Agent FrameworksStrong Python ExperienceArchitectural JudgementStrong Product Sense

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Applicant Tracking System Keywords

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Hard Skills

Agent FrameworksPythonSQLRelational ModellingCI/CDTestingVersion ControlEmbedding ModelsVector StoresRAG in Production

Soft Skills

Direct CommunicationExpertise SharingProblem-Solving

Tools & Technologies

Azure AI ServicesGCPAWSLangGraphLlamaIndexSemantic KernelADK

Industry Keywords

Agentic SystemsLLM-Powered SystemsRetrieval PipelinesData-Driven Decision MakingUser Experience

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonSQL

About the role

Key responsibilities & impact

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.
Turn the experiments that earn it into agents we're happy to put in front of players, tested and validated to the standard a live product demands.
Design for the failure cases: a call that times out, a tool that errors, a job that runs for an hour, a decision the agent should hand back to a person.
Build the shared frameworks, templates and infrastructure that let other engineers ship their own agents without starting from scratch.
Set the technical shape of our agent estate: how we retrieve, how we evaluate, how we constrain behaviour, how we see what's happening, and how any of it reaches production.
Decide what needs sign-off before an agent goes live, how it handles player data, and where its authority stops.
Leave a clear trail behind every agent: what it does, why we promoted it, what it costs, and whether the return still justifies it.
Be Stakemate's AI champion: office hours, demos and short training sessions that make the wider team genuinely better at using AI.
Partner with department leads on where AI helps, and where it doesn't.

Requirements

What you’ll need

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.
Must-have
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 (LangGraph, LlamaIndex, 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.
Nice-to-have
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.

Benefits

Comp & perks

Competitive salary reflective of the strategic importance of this role.
Meaningful equity package: you're building something valuable, you should own a piece of it.
Hybrid model Mon/Tue/Thu in our 79-81 Borough Rd office.
Direct access to the senior leadership team and real strategic influence.
Substantial opportunity for career growth as the company scales.