Senior GenAI Full-Stack Engineer - Brazil
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 1d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 5d ago
The date the source published, not the day we noticed it (2026-10-06). Last seen at its source 2h ago.
We have tracked this listing since 6 Oct 2026 (4 days). The employer's own board has carried it every time we have read it, most recently 2 hours ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
Brazil, Portugal
The description states no restriction of its own. This is the source's own tag.
Pay not stated
Similar roles pay $93.8k–157.1k/yr
Middle 50% of 16 listings that do state pay — Engineering · Senior · Brazil · USD/year. This employer has published no salary; this is what comparable listings we hold disclose, never converted between currencies or periods. How this is calculated.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Workable employer's own board first seen 4d ago · last seen 2h ago
The listing
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