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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 agreat 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.
CargoSprintis 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, 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
Technical Skills
- 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
- FastAPIand 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
Stack
You will work with these technologies day to day. Strong candidates will be familiar with most of them — full fluency across every tool is not expected from day one.
- Backend: Python · FastAPI · Temporal
- Database: PostgreSQL
- Agents & RAG: LandGraph LLMs(Open AI, Claude) Embeddings
- DevOps: Docker · Kubernetes · Pulumi
- Integrations: Salesforce Hubspot REST APIs
Compensation and benefits
We offer competitive pay and benefits designed to fuel our team's success.
- Health and Wellness: Medical, dental, and vision plans for you and your family
- Future-Ready: 401(k) with company match
- Work Life Balance: Generous flexible PTO program and paid holidays
- Grow With Us: Professional development opportunities
Does this role sound like the next step in your career?
We’d love to hear from you! If you don’t meet all of the requirements exactly, we encourage you to use your cover letter to tell us about your unique experience—talent comes from many places, and skills are transferable.
Our Commitment to an Extraordinary Work Environment
At CargoSprint, we value diversity and inclusivity. We strive to create a welcoming and supportive community for employees from all backgrounds. Regardless of your gender, sexual orientation, physical ability, religion, ethnicity, race, or age, you will find a place where you can thrive and be your authentic self.
Our CargoSprint Recruitment Team personally reviews every application.
