# Senior GenAI Full-Stack Engineer - Brazil **Company:** [Codurance](https://jobs.workable.com/companies/igLtyg2L5MrPb2f9wqNdG3.md) **Location:** Remote **Workplace:** remote [Apply for this job](https://jobs.workable.com/view/d6af3e39-5a60-41e9-9774-27b74cbedd2a) ## Description 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