Senior GenAI Full-Stack Engineer - Brazil

Bot Jobs; Myrtle Point (OR); Remote; USD 60,000 - 108,000; Full time

Job summary

Codurance in Brazil is seeking a Senior GenAI Full-Stack Engineer to lead production LLM-powered systems, spanning NestJS APIs and React UIs, with emphasis on tool-calling, RAG pipelines, and observability.

You will work on model selection, latency/cost tradeoffs, and deploys with Langfuse, BullMQ, and Redis in a remote Brazil setting.

Qualifications

• Proven experience building production LLM-powered systems like chatbots or agents.
• Strong TypeScript/Node.js engineering; strict-mode fluency.
• Experience shipping AI features: prompts, tool calling, evaluation.
• Fullstack depth across NestJS APIs, React UIs, databases and infra.
• Ability to balance model quality, latency, throughput and cost.
• Troubleshoot AI systems across prompts and pipelines.
• State machine thinking with async workflows (XState).
• REST APIs, queues, events, and caching strategies.
• Solid testing: unit, integration, contract tests.

Responsibilities

• Build and operate production LLM-powered systems like chatbots and copilots.
• Develop and evolve the AI stack including tool use and context handling.
• Integrate with MCP for model-context driven tool usage.
• Evaluate and benchmark providers (OpenAI, Gemini, Anthropic).
• Tune models for quality, latency, and cost; manage performance issues.
• Instrument with Langfuse, A/B tests, and telemetry.

Skills

TypeScript

Node.js

Fullstack

REST APIs

XState

Testing

Monorepo

Caching

Redis

PostgreSQL

Observability

Education

Bachelor's degree in Computer Science or related

Tools

NestJS

React

BullMQ

Langfuse

Kysely

OpenAI / Gemini / Anthropic API

Job description

Senior GenAI Full-Stack Engineer - Brazil

at Codurance

Remote Brazil Posted few hours ago

Description

• 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