Senior GenAI Full-Stack Engineer - Brazil
LocationPortugal
Typefull-time
SenioritySenior
DepartmentEngineering
Company size51–200 people
PostedOct 6, 2026 · 6d ago
Verified live17h ago
At a glanceSummarised by Seekless from the posting.
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
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
Skills
NestJS
XState v5
OpenAI SDK
ajv
Epicenter admin
MCP
OpenAI
Gemini
Anthropic
Langfuse
BullMQ
Redis
PostgreSQL
Kysely
TypeScript
Node.js
React
REST API
Portkey
Responsibilities
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
Nice to have
•
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