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