Senior GenAI Full-Stack Engineer - Brazil

Codurance; Portugal; On-site; EUR 90,000 - 130,000; Full time

Job summary

Codurance is seeking an experienced AI/LLM engineer to design and operate production-grade LLM-powered systems and agent copilots. You will build scalable workflows using NestJS, XState, and OpenAI SDK, handling RAG, intents, tool calls, and human-in-the-loop logic.

You will own observability, model evaluation, and performance tuning across providers, while ensuring low latency, reliability, and cost effectiveness in real-world customer support scenarios.

Qualifications

• 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
• Strong testing culture: unit, integration, and contract tests are first-class deliverables, not afterthoughts
• Experience working in a monorepo with multiple interconnected services

Responsibilities

• Design and extend production-grade LLM applications and agentic workflows using NestJS, XState v5, and the OpenAI SDK
• Build and maintain the conversation-machine substrate: guard/action registries, flow validation, DB-driven flow configs, and design-time tooling
• Develop AI systems behind Support Assistant and Agent Support Assistant used by customers and agents
• Integrate with MCP servers for tool-use and agentic behaviours
• Evaluate, benchmark, and tune models across 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 outages
• Build resilience mechanisms: retries, fallback routing, caching, streaming, rate limiting, and provider routing
• Instrument and tune model quality using Langfuse, evals, prompt management, datasets, A/B testing, versioning, and production telemetry
• Manage async workloads via BullMQ and caching with Redis; PostgreSQL persistence via Kysely

Skills

TypeScript/Node.js

Production AI systems

RAG pipelines

Tool calling

Observability

Langfuse

Redis/BullMQ

Kysely

Tools

NestJS

XState

OpenAI SDK

Job 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
• Build and evolve the AI systems behind Support Assistant , 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 behaviours
• 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