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

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