Senior Artificial Intelligence Specialist

GR8_TECH builds B2B iGaming platforms for operators who play to lead. They deliver full-cycle, high-impact technology designed to scale, powering millions of active players and driving real business growth. The company has over 1000 employees across multiple locations and time zones.

Must-have:

• Strong software engineering background.
• Proven experience building LLM-based systems in production.
• Hands-on experience with LLMs and RAG architectures, chatbots or conversational AI systems, tool/function calling, and agent-style patterns.
• Experience with context or tool-serving systems (MCP or similar).
• Experience integrating AI into real-time production products.
• Understanding of performance, scalability, and cost trade-offs.
• Cloud experience, preferably AWS.
• Strong engineering fundamentals including testing, debugging, reviews, and observability.

Nice-to-have:

• Experience with multi-agent or workflow-based systems.
• Fine-tuning or adapting open-source LLMs.
• ML platforms / MLOps background.
• Experience operating latency- or cost-sensitive systems.
• Production incident ownership in AI systems.

Technical Ownership:

• Own LLM-based systems from design to production support.
• Design architectures for Generative AI systems (RAG, agents, tool/context serving).
• Make trade-offs between quality, latency, cost, and complexity.
• Set engineering standards for building and operating AI systems in production.
• Act as a technical reference point for applied Generative AI.

Hands-on AI Engineering:

• Build and maintain production LLM-powered systems.
• Develop chatbots and AI assistants with predictable behavior.
• Design and implement RAG pipelines (ingestion, embeddings, retrieval, generation).
• Implement agent-style workflows with tool/function calling.
• Build and integrate MCP servers or similar context/tool-serving components.
• Integrate AI systems with backend services and ML infrastructure.

Production & Reliability:

• Design evaluation frameworks for LLM outputs.
• Monitor LLM behavior, system health, and costs in production.
• Address performance, scalability, and operational issues.
• Handle production incidents and contribute to long-term fixes.
• Design systems with failure modes, fallbacks, and graceful degradation.