巴西高级通用人工智能全栈工程师

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