Head of Automation Engineering

About the Company

Our Client is your go-to iGaming service provider. They’re here to empower iGaming operators, allowing them to thrive in such a volatile industry. Their years of expertise, cutting-edge technology, and passion for delivering extraordinary player experiences make them the right choice for any company looking to elevate their success. They offer a range of iGaming offerings such as; Gaming Services, Casino Marketing, Sportsbook Marketing and Operations, Creative, Data and B.I, Operations (CS, RPF, KYC), VIP Management, Acquisition, Social Media, Content, Product, Legal and Compliance.

The Role

Our Client is redefining the iGaming industry by embedding intelligence into every layer of our operations. As Head of AI Automation & Agentic Systems, you will architect and lead the group’s end-to-end AI automation capability — from bare-metal infrastructure through to autonomous, multi-agent workflows that power our Casino, Sportsbook, Marketing, Operations, and Compliance functions.

This is a hands-on leadership role for someone who can move fluently between evaluating open-source model benchmarks, designing cost-optimised inference stacks, and steering the business case for AI-driven transformation at the executive level. You will own the full lifecycle: build the platform, select and deploy the models, identify the highest-value use cases, and drive measurable ROI across a diverse group of companies.

Key Responsibilities:

• Infrastructure & Platform

• Configure and manage cloud-based AI service integrations (e.g. OpenAI, Anthropic, Google) for appropriate use cases, while actively reducing third-party dependency for core operational workflows.

• Deploy and operate self-hosted open-source models (e.g. Llama, Mistral, Qwen, Phi families) using serving frameworks such as Ollama or vLLM, across cloud or on-premise environments.

• Design lightweight orchestration layers that connect both hosted and self-hosted AI services to internal systems, data sources, and operational workflows.

• Define and enforce security, access control, and data governance standards for all AI systems — with particular attention to data residency and sovereignty when handling sensitive player and operational data.

• Implement monitoring and observability across all AI integrations — tracking uptime, latency, token consumption, and cost per workflow.

• Model Selection & Evaluation

• Maintain a structured evaluation framework for benchmarking open-source models (Llama, Mistral, Qwen, Gemma, Phi, and emerging releases) against closed-source alternatives across quality, speed, and total cost of ownership.

• Default to open-source, self-hosted models for operational use cases — reserving third-party API calls for tasks where the capability gap genuinely justifies the cost and dependency.

• Match model capabilities to specific business tasks — knowing when a self-hosted 8B model is the right call and when a frontier API is unavoidable.

• Design retrieval-augmented generation (RAG) architectures that ground model outputs in company data, reducing hallucination and improving task relevance.

• Stay current with the rapidly evolving open-source landscape and translate new model releases into concrete operational improvements across the group.

• Cost Optimisation & Governance

• Own the AI cost model across the group: track spend per workload, establish unit economics, and enforce budget guardrails — with open-source self-hosting as the primary lever for reducing ongoing operational cost.

• Apply cost-reduction techniques including model routing (self-hosted vs. third-party tiers), prompt compression, caching strategies, and batching architectures.

• Produce clear ROI reporting for each AI initiative, linking automation output to measurable business outcomes (headcount efficiency, revenue impact, risk reduction).

• Build and maintain a vendor and model evaluation process that continuously challenges reliance on paid APIs — migrating workflows to self-hosted alternatives as open-source capability matures.

• Leadership & Collaboration

• Build a lean, high-calibre automation engineering team from the ground up — defining the hiring profile, team structure, onboarding standards, and ways of working suited to a fast-moving, output-focused function.

• Lead the team with a bias for delivery: set clear priorities, remove blockers, and maintain a culture where working systems ship regularly rather than ideas circulate indefinitely.

• Establish engineering standards for the team covering code quality, agent design patterns, testing practices, documentation, and operational handoff.

• Translate complex agentic AI concepts into clear business language for non-technical stakeholders and executive audiences.

Requirements:

• 7+ years experience in software engineering or infrastructure roles, with at least 3 years focused on AI/ML systems in a production environment.

• Prior experience in a technical leadership or principal engineer capacity is required.

Nice to Have:

• Experience in iGaming, fintech, or another regulated, data-intensive industry.

• Familiarity with compliance automation use cases: AML monitoring, responsible gambling tooling, regulatory reporting.

• Experience working across a group or holding company structure spanning multiple products and jurisdictions.

• Contributions to open-source AI projects or published technical writing on AI systems design.

You receive:

• Attractive remuneration package

• Health insurance cover from the first day of work

• Wellness benefit (after probation)

• Optician/Spectacle and Blue Lens Benefit (after probation)

• Breakfast/lunch all week

• Monthly snacks allowance

• Training support

• Modern office facilities

• Dog-friendly workplace

• Exciting Company Events

• Monthly Beer Fridays

• Eur1,000 Refer a friend bonus

• Relocation package (if required)

• One day birthday holiday