AI Solution Engineer
The AI Solution Engineer role focuses on building practical AI tools, prototypes, and applications that improve workflows, products, and productivity. This position involves designing agentic workflows that automate tasks, use APIs, and support complex processes. The role requires staying current with AI trends, turning insights into demos and recommendations, and partnering with teams to turn ideas into real solutions. It aims to drive efficiency and productivity through AI across multiple business units, improve solution quality, cost, and performance through better design and optimisation, and identify and mitigate AI risks, ensuring safe and responsible use. The role also involves contributing to AI standards, governance, and best practices, as well as sharing knowledge and mentoring others to raise the overall AI engineering bar.
- 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)
- Hybrid working style – work in our modern and comfortable office as well as work from home office.
- Neat benefits & bonus package.