AI Engineer

Brightstar is an innovative, forward-thinking global leader in lottery that builds on our renowned expertise in delivering secure technology and producing reliable, comprehensive solutions for our customers. As a premier pure play global lottery company, our best-in-class lottery operations, retail and digital solutions, and award-winning lottery games enable our customers to achieve their goals, fulfill player needs and distribute meaningful benefits to communities. We have a longstanding commitment to Responsible Gaming (RG) that is engrained within our core business and the products we offer to customers and players worldwide. Brightstar has a well-established local presence and is a trusted partner to governments and regulators around the world, creating value by adhering to the highest standards of service, integrity, and responsibility.  Brightstar has approximately 6,000 employees. For more information, please visit  .

Role Overview:

We develop and operate a highly data-intensive platform offering analytics, machine learning and agentic AI on cloud environments. As part of continued investment, we are building a cutting-edge AI-native decision-system.

As an Agentic AI Developer, you will be responsible for designing and developing advanced agentic AI solutions capable of autonomous planning, reasoning, and task execution. developing and integrating Retrieval-Augmented Generation (RAG) pipelines to enhance agents' reasoning capabilities with external knowledge sources, multi-agent communication protocols to enable seamless collaboration and coordination among agents. Collaboration will be key, as you work cross-functionally with Data Scientists, ML Engineers, and Product Teams to deliver robust, end-to-end AI solutions

You will work at the intersection of cutting-edge AI, Data Science and modern software engineering, implementing multi-agent frameworks and orchestrating interactions

Key Responsibilities:

• Design and implement Agentic AI architectures for enterprise workflows.
• Integrate Generative AI capabilities (LLMs, multimodal models) into client solutions.
• Deliver end-to-end AI solutions from ideation to production deployment.
• Build, fine-tune, and evaluate LLM-based Q&A models using frameworks like LangChain, HuggingFace Transformers, or OpenAI API.
• Design prompt templates and implement retrieval strategies to increase answer precision and factuality.
• Collaborate with product managers to translate user requirements into technical features.
• Participate in error analysis, iterative model improvement, and performance tuning.
• Engage with clients to identify high-value AI use cases and define business benefits.
• Conduct workshops and assessments to align AI strategies with organisational goals.
• Provide thought leadership on AI adoption and emerging trends.
• Develop reusable frameworks and accelerators for Agentic AI and GenAI.
• Ensure compliance with AI ethics, security, and governance standards.
• Stay on top of industry developments in Agentic AI, autonomous agents, and LLM ecosystems.
• Orchestrate complex multi-agent workflows to handle tasks requiring planning, reasoning, and tool use.
• Extend Microsoft 365 Copilot by building custom plugins and declarative agents within Microsoft Copilot Studio to surface enterprise data in Teams and Office apps.
• Operationalize AI solutions using Microsoft AI Foundry for model catalog management, Prompt Flow evaluation, and lifecycle governance.
• Architect scalable deployment patterns for agents using Azure Container Apps or Azure Functions, ensuring low-latency responses and cost-effective scaling.

Platforms and interfaces

• Own data flows, APIs, services, model-serving surfaces, front-end and desktop application surfaces, continuous integration and continuous delivery (CI/CD), and demo hardening.
• Build the systems that make quantitative work feel polished, reliable, and enterprise-ready for expert users and client stakeholders.

Agent-assisted systems

• Own the agentic harness layer — evaluation frameworks, reviewer loops, control-plane behavior, orchestration, and tool integration — that applications and MCPs wrap around.
• Design opinionated harnesses that expose through MCP or similar integration patterns without overfitting to one vendor or one moment in the tooling market.

Requirements:

• Strong experience in Agentic AI frameworks (e.g., LangGraph, AutoGen, CrewAI).
• Hands-on expertise with