Responsibilities - Design and own the agent systems architecture — retrieval, orchestration, tool integration, and evaluation — as a coherent, production‑grade platform - Build RAG pipelines that ground agents in real CargoSprint data: indexing strategies, chunking, embedding models, retrieval evaluation, and freshness maintenance - Design orchestration patterns for multi‑step agentic workflows using LangGraph or equivalent — with explicit attention to failure modes, non‑determinism, and graceful degradation - Build and maintain the tool and integration layer that connects agents to production systems — Salesforce, HubSpot, Postgres, internal APIs — with the error handling and retry logic that production demands - Instrument everything: distributed tracing, latency dashboards, retrieval quality metrics, LLM output evaluation pipelines - Establish reusable agent primitives and internal engineering patterns so the team builds the next agent faster and more reliably than the last one - Partner with the engineers building individual agents to review architectures, catch design mistakes early, and raise the overall quality bar - Travel to CargoSprint's Guadalajara office as needed to work directly with the operational teams whose workflows the agents are being built around - Use AI coding tools to accelerate your own development and set the standard for how the team works with them Requirements - 8+ years of engineering experience, with meaningful time spent building systems that run reliably under real production load - A track record of technical decisions you made, owned, and lived with — including the ones that turned out to be wrong and what you did about them - Strong business judgment — you understand that a technically elegant agent nobody uses is a failure. You can read a workflow, identify the real cost, and design for adoption, not just correctness.





- Excellent communication in English — you can explain a retrieval architecture to a product manager and a vector indexing strategy to a staff engineer, and you know which explanation to give in which room - Willingness to travel to CargoSprint's Guadalajara, Mexico office as needed — the workflows you are designing systems for live there, and understanding them firsthand matters - Expert‑level Python — idiomatic, well‑tested, production‑grade. You write code that the next engineer can understand and extend.
- Deep RAG system design experience — you have designed and operated retrieval pipelines in production: chunking strategies, embedding model selection, hybrid search, re‑ranking, context window management, and retrieval evaluation. You know the failure modes intimately.
- Agent orchestration architecture — LangGraph, LangChain, or equivalent; you have designed multi‑step agentic workflows with tools, memory, branching logic, and human‑in‑the‑loop patterns that are predictable under real usage - LLM integration and prompt engineering — you understand how to structure prompts for reliability, how to version and evaluate them, and how to manage the gap between model capability and production behavior - Vector databases and search infrastructure — pgvector, Pinecone, Weaviate, or equivalent; you know when to use dense vs. sparse retrieval and how to build an evaluation harness to measure retrieval quality - FastAPI and backend service design — you build the infrastructure your agent systems run on with the same rigor as the systems themselves - Observability and production operations — distributed tracing, structured logging, alerting, LLM‑specific evaluation pipelines; you know what good looks like before something breaks - DevOps fundamentals — Docker, Kubernetes, CI/CD; you own what you ship all the way to production.

📌 Staff Engineer, Agent Systems (Puerto Vallarta)
🏢 Jobtailor
📍 Puerto Vallarta