Senior GenAI Full-Stack Engineer - Brazil

Sobre a oportunidade

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

Originally posted on Himalayas