AI Solution Engineer
Playtech is hiring AI Solutions Engineers who enjoy turning ideas into practical solutions, experimenting with the latest tools, and solving real business problems. This is not about theory - it’s about building AI that works in the real world! The AI Solution Engineer role focuses on building practical AI tools, prototypes, and applications that improve workflows, products, and productivity. The position involves designing agentic workflows, establishing reusable patterns, templates, and best practices for AI adoption across Playtech, and partnering with various teams to deliver real-world solutions. Engineers in this role will stay current with AI trends, turn insights into demos, workshops, and recommendations, and drive efficiency and productivity through AI across multiple business units. They will also improve solution quality, cost, and performance through better design and optimisation, identify and mitigate AI risks, and contribute to AI standards, governance, and best practices. Sharing knowledge, mentoring others, and raising the overall AI engineering bar are also key aspects of the role.
- Build practical AI tools, prototypes, and applications that improve workflows, products, and productivity.
- Create reusable patterns, templates, and best practices for AI adoption across Playtech.
- Design agentic workflows that automate tasks, use APIs, and support complex processes.
- Stay current with AI trends and turn insights into demos, workshops, and recommendations.
- Partner with teams to turn ideas into real solutions and support AI adoption hands-on.
- Drive efficiency and productivity through AI across multiple business units.
- Improve solution quality, cost, and performance through better design and optimisation.
- Identify and mitigate AI risks, ensuring safe and responsible use.
- Contribute to AI standards, governance, and best practices.
- Share knowledge, mentor others, and raise the overall AI engineering bar.
- Have a strong interest in AI, especially enterprise AI solutions, and genuine enthusiasm to learn and keep pace with the latest developments and trends. (required)
- Bring a strong engineering background, with hands-on experience building and deploying AI, ML, or LLM-based systems. (required)
- Have experience designing platform-level capabilities (APIs, SDKs, shared services) that other engineering teams consume. (required)
- Are comfortable working across teams, explaining trade-offs, mentoring others, and partnering with various stakeholders. (required)
- Have a practical understanding of modern AI tooling: LLM APIs, vector stores, RAG, evals, agent frameworks, and developer assistants such as Cursor. (required)
- Understand software architecture and version control (Git), whether from a development background or equivalent hands-on AI experience. (required)
- Bring a pragmatic, delivery-focused mindset; comfortable in a large, regulated, multi-product engineering environment. (required)
- Familiarity with Google Cloud AI services (Gemini Enterprise Agent Platform, ADK). (nice-to-have)
- AI evaluation or AI security experience. (nice-to-have)
- Agentic development experience (MCP, multi-agent systems). (nice-to-have)
- RAG implementation experience. (nice-to-have)
- Cloud DevOps or Terraform experience. (nice-to-have)
- Prior experience in iGaming, fintech, or another regulated industry. (nice-to-have)
- AI governance experience or interest. (nice-to-have)
- Coding experience, especially in Python. (nice-to-have)
- Neat benefits & bonus package.
- Hybrid working style – work in our modern and comfortable office as well as work from home office.