Senior GenAI Full-Stack Engineer - Brazil

Design and extend production-grade LLM applications and agentic workflows using NestJS, XState v5, and the OpenAI SDK — flows include RAG, intent detection, clarification, fulfillment, escalation, tool-use, and human-in-the-loop state machines - Build and maintain the conversation-machine substrate: guard/action registries, flow validation (ajv), DB-driven flow configs, and design-time tooling in Epicenter admin - Build and evolve the AI systems behind Epic Support Assistant (ESA), the player-facing support chatbot, and Agent Support Assistant, the AI copilot used by customer support agents - Integrate with MCP servers (Model Context Protocol) for tool-use and agentic behaviors - Evaluate, benchmark, and tune models across providers including OpenAI, Gemini, Anthropic, and future providers; own model selection decisions balancing quality, latency, throughput, reliability, and cost - Troubleshoot production LLM issues including hallucinations, retrieval failures, prompt regressions, model drift, token inefficiencies, latency bottlenecks, and provider outages - Build resilience mechanisms: retries, fallback routing, caching, streaming, rate limiting, and provider routing - Instrument and tune model quality using Langfuse (tracing, evals, prompt management), evaluation datasets, A/B testing, prompt versioning, and production telemetry - Manage async workloads via BullMQ and caching with Redis; PostgreSQL persistence via Kysely Requirements Must-Have - Proven experience building and operating production LLM-powered systems similar in scope to chatbots, AI assistants, agent copilots, RAG systems, or LLM orchestration platforms - Strong TypeScript/Node.js engineering; TypeScript strict-mode fluency - Production AI experience: prompt engineering, RAG pipelines, agent design, tool calling, model evaluation, observability, and failure-mode analysis — you've shipped AI features, not just prototyped them - Fullstack depth: comfortable moving between NestJS APIs, React UIs, databases, infrastructure, and production operations; you don't artificially limit yourself to one layer - Ability to evaluate tradeoffs between model quality, latency, reliability, throughput, and cost - Ability to troubleshoot AI systems across prompts, retrieval pipelines, model configuration, infrastructure, and application code - State machine thinking — you naturally model complex async workflows; XState or similar experience is a strong signal - Solid understanding of REST API design, async patterns (queues, events), and caching strategies - Strong testing culture: unit, integration, and contract tests are first-class deliverables, not afterthoughts - Experience working in a monorepo with multiple interconnected services Strong Plus - Hands-on experience with MCP (Model Context Protocol) or building tool-use agentic workflows - Familiarity with Langfuse or other LLM observability/evaluation platforms - Experience operating AI workloads at scale - Experience evaluating multiple foundation models and providers - Experience building AI copilots, assistants, or conversational products - Experience with semantic search and retrieval architectures - Experience with AI gateways such as Portkey or similar platforms - Experience with NestJS specifically: modules, providers, guards, interceptors, DI patterns - Background in customer support or player support platforms — you understand the stakes of getting AI-generated responses wrong - Experience shipping under low-latency constraints (chatbot response time budgets, streaming) - Previous work in gaming or high-volume consumer products