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.
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📌 Staff Engineer, Agent Systems (Jalisco)
🏢 Jobtailor
📍 Jalisco