Technical Architect (AI)

The role

A fast-growing B2B iGaming platform provider is moving artificial intelligence from experiment to core capability, and they want a Technical Architect (AI) to own exactly how that happens. This is not a job about bolting an LLM onto a single feature and calling it innovation. It is about defining, end to end, how AI becomes a secure, scalable and measurable part of the platform: the architecture, the reusable patterns, the guardrails and the infrastructure that let engineering teams ship large language models, retrieval-augmented generation, vector search and agents into production without introducing risk to a regulated business.

You'll sit at the intersection of architecture, engineering, product, security and data, and you'll carry genuine authority across all of them. On any given day you might be choosing between a hosted and a self-managed model, designing a permission-aware retrieval pattern, setting a token budget for a new feature, or explaining a complex trade-off to a non-technical stakeholder in language they can act on. The common thread is turning fast-moving AI opportunities into production-ready designs that are performant, cost-aware, compliant and safe.

Success here is practical, not theoretical. It looks like reusable patterns that move teams from a promising prototype to governed, production-grade delivery; controls that protect sensitive operational and player data by default; and AI that genuinely lands across internal tooling, knowledge management, operator-facing capabilities and platform automation. Above all, you'll be the person who makes the hard AI trade-offs visible, understandable and decisive for the whole organisation.

What you'll own

AI architecture & enablement

You'll own the technical direction for every LLM-enabled capability on the platform, from model selection and hosted-versus-self-managed decisions to AI service boundaries, API contracts and integration patterns. You'll define and maintain the AI architecture blueprint the wider engineering organisation builds against, covering LLM enablement, retrieval-augmented generation, shared AI services, security guardrails, model evaluation, observability and cost control.

Retrieval & knowledge

You'll design secure retrieval-augmented generation from the ground up: embeddings, vector databases, semantic search, metadata filtering, chunking strategy, permission-aware retrieval and the full knowledge lifecycle. Getting this right is what separates a convincing demo from a system that returns the right answer to the right person without leaking anything it shouldn't.

Safety in a regulated market

In a real-money, regulated environment, safety is non-negotiable. You'll set the controls that keep AI trustworthy, addressing prompt injection, data leakage, hallucination risk, PII handling, model access control, audit logging, sensitive-data redaction and output validation, and you'll make those controls easy for engineers to adopt rather than a tax they route around.

Infrastructure & cost

You'll architect AI workloads for high availability, low latency, throughput and resilience across AWS, Kubernetes and EKS, and event-driven platform patterns. Just as importantly, you'll treat tokens, inference and infrastructure as first-class architectural constraints, establishing principles for prompt design, context management, model routing, caching, batching, summarisation, retrieval precision and token-budget governance, so AI scales commercially as well as technically.

Evaluation, integration & leadership

You'll define the model evaluation frameworks, acceptance criteria, regression testing, monitoring and feedback loops that tell the business whether an AI feature is actually working, and connect AI into the core Player Account Management platform, CMS, integration hub, data lake, support tooling and knowledge systems using pragmatic, maintainable architecture. Leadership runs throughout: you'll produce architecture decision records, specifications, target-state diagrams and non-functional requirements, mentor engineers, and guide implementation through clear trade-off analysis with product, engineering, QA, DevOps and security.

What success looks like

In your first few months you'll get under the skin of the platform and the team's current AI experiments, then set a target-state architecture everyone can rally around. Over time, success means engineering teams reaching for your patterns by default, AI features shipping with governance built in rather than bolted on afterwards, and measurable improvements in the cost and reliability of every AI workload on the platform.

Who you'll work with

You'll work alongside experienced engineers, data specialists, product owners and security colleagues across a distributed, remote-first organisation. This is a hands-on architecture role with real influence, not an ivory-tower one, close enough to the code to be credible, senior enough to set direction.

Nice to have

• Background in iGaming, sports betting, PAM platforms, payments, KYC/AML or responsible gaming
• Orchestration frameworks: LangChain, LlamaIndex, LangGraph, Semantic Kernel, MCP or CrewAI
• LLMOps/MLOps, model gateways, prompt and version management, AI observability
• AWS, security or AI certifications
• OWASP Top 10 for LLM Applications, NIST AI Risk Management Framework or similar

What's on offer

A senior AI platform architect role with genuine architectural influence and a salary of circa €100,000 per annum. It's fully remote and open to candidates based anywhere in the UK or EU, with flexible working that supports a healthy work-life balance and levels the playing field for people with caring responsibilities. You'll join a collaborative, international environment alongside highly experienced engineers and domain specialists, with professional development and support from day one, and the autonomy to set AI direction across platform, data, product and engineering teams.

About the client

The client is an established B2B iGaming solutions provider whose enterprise technology is built around a core Player Account Management (PAM) platform, covering player accounts, wallet, CRM, bonus management, payments, KYC/AML and fraud-prevention services for operators across regulated markets. They operate a remote-first culture with distributed engineering teams, and are committed to building diverse, inclusive teams, welcoming applications from people of all backgrounds and experiences.

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