Staff Engineer, Agent Systems

Empowering the people that make global commerce happen.

About CargoSprint

CargoSprint is made up of a world-class team of highly motivated individuals who are passionate about transforming the cargo industry. We have developed cutting-edge digital solutions that streamline cargo operations, enhance efficiency, and improve the overall experience for everyone involved. Our workplace fosters innovation, collaboration, and the drive to solve industry challenges.

CargoSprint is dedicated to delivering game-changing solutions that connect the cargo industry like never before, and we are looking for driven, enthusiastic people who share our vision of innovation and excellence.

If you think we are a great mutual fit, we want hear from you!

About You

You think in systems. When someone describes a workflow problem to you, you are already modeling the retrieval architecture, the orchestration graph, the failure modes, and the observability layer — before they finish the sentence.

You have built agent systems that run in production, not just in demos. You understand what makes agentic workflows fail — context bleed, retrieval drift, non-deterministic tool calls, silent degradation — and you design defensively against all of it. You have strong opinions about agent architecture because you have been burned by the wrong decisions and learned from them.

You are passionate about solving complex problems and believe in lifelong learning, constantly staying on the cutting edge of what is possible with LLMs, retrieval, and agent orchestration. And you want to do it somewhere that will actually put your work into production and measure whether it worked.

About the role

Most companies are building AI demos. We are building agent infrastructure.

CargoSprint is deploying production agents across our internal operations — sales, finance, customer ops, engineering productivity — and we need a Staff Engineer who can design the systems that make those agents reliable, observable, and genuinely useful.

You will own the agent systems layer: the retrieval pipelines that ground our agents in real organizational knowledge, the orchestration architecture that makes multi-step workflows predictable, the tooling and APIs that connect agents to the systems our teams run on, and the observability layer that tells us when something is degrading before a user notices.

What you'll do

• 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

Qualifications

General

• Extreme ownership — you care about what happens to the systems you build after they ship. Degradation, drift, and silent failures are personal.

• 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,