Lead AI Engineer (LATAM Remote) at UP.Labs

United States

<h3><strong style="background-color: transparent; color: rgb(0, 0, 0);">Overview</strong></h3><div><span style="background-color: transparent; color: rgb(0, 0, 0);">UP.Labs is a dynamic venture studio dedicated to building innovative startup companies from the ground up. Our team thrives on solving complex problems, driving technological advancements, and creating impactful digital products. We're seeking a highly skilled professional to join our growing team and contribute to our mission of launching the next wave of successful startups.</span></div><h3><br></h3><h3><strong style="background-color: transparent; color: rgb(0, 0, 0);">Technical Challenge</strong></h3><div><span style="background-color: transparent; color: rgb(0, 0, 0);">We're building an agentic system as the core delivery mechanism for one of our ventures, and we're looking for a production agent-systems engineer who will own and grow the agentic function end-to-end — not a notebook builder or an ML researcher. You'll design the architecture for agents across the business, ground them in a complex proprietary domain through strong context and retrieval engineering, and build systems that take real action for real users, not just chatbots.</span></div><h3><br></h3><h3><strong style="background-color: transparent; color: rgb(0, 0, 0);">Responsibilities</strong></h3><ul><li class=""><span style="background-color: transparent;">Own and grow the agentic function end-to-end: set architecture across internal (data-engineering &amp; data-science agents) and external (customer/partner-facing agents, MCP servers) surfaces, make build-vs-buy calls, and grow a team under you as we scale.</span></li><li class=""><span style="background-color: transparent;">Design, build, and deploy production LLM agents that take consequential action (write-back, execute changes) with human-in-the-loop controls — including tool interfaces (MCP, function calling), tiered tool access, approval gates, and rollback.</span></li><li class=""><span style="background-color: transparent;">Build and maintain eval harnesses for agentic systems — offline and online evaluation, regression testing for non-determinism, guardrails, and observability/tracing.</span></li><li class=""><span style="background-color: transparent;">Own context and retrieval engineering: go beyond naive chunking to build high-quality retrieval, context assembly, and grounding in proprietary domain data to reduce hallucination.</span></li><li class=""><span style="background-color: transparent;">Implement graph-based knowledge and retrieval systems (GraphRAG, property graphs, ontology/semantic layers) to ground agents in a complex, structured domain.</span></li><li class=""><span style="background-color: transparent;">Partner with applied science and the broader data/ML function on pipelines, embeddings, and vector stores where relevant, without owning ML model training.</span></li><li class=""><span style="background-color: transparent;">Communicate agent architecture, trade-offs, and roadmap to execs and investors; act as a player-coach who writes production code today while owning the function's direction and hiring as the team grows.</span></li></ul><h3><br></h3><h3><strong style="background-color: transparent; color: rgb(0, 0, 0);">Required Skills</strong></h3><ul><li class=""><span style="background-color: transparent;">5+ years shipping production software; strong full-stack/backend engineering (Python core, TS/Node or Go a plus), including production APIs, data models, testing, and CI.</span></li><li class=""><span style="background-color: transparent;">Proven track record building and shipping LLM agents to production with real users — multi-step, tool-calling, stateful, with orchestration (LangGraph or equivalent) — and the ability to explain the control loop, not just the framework used.</span></li><li class=""><span style="background-color: transparent;">Experience building eval harnesses for agentic systems (e.g. MLflow, LangSmith, or custom) with fluency in determinism, drift, and guardrails.</span></li><li class=""><span style="background-color: transparent;">Experience owning a retrieval system in production, including chunking vs. structured retrieval trade-offs and evaluating retrieval quality.</span></li><li class=""><span style="background-color: transparent;">Experience shipping agents that take consequential, real-world action, with a clear point of view on approval/guardrail/rollback architecture (bonus: an incident where the agent did the wrong thing, and how it was handled).</span></li><li class=""><span style="background-color: transparent;">Track record leading an agentic initiative or team end-to-end — from architecture to production — including communicating agent systems to both execs and investors.</span></li></ul><h2><br></h2><h3><strong style="background-color: transparent; color: rgb(0, 0, 0);">Preferred Skills</strong></h3><ul><li class=""><span style="background-color: transparent;">Experience shipping graph-backed retrieval and explaining why graphs outperform flat vectors for structured domains (GraphRAG, knowledge graphs, ontologies, property graphs, triple stores).</span></li><li class=""><span style="background-color: transparent;">Experience fine-tuning or distilling an open-source model (LoRA/QLoRA) with measured gains, or strong context/prompt optimization as a substitute.</span></li><li class=""><span style="background-color: transparent;">Experience serving/operating open-source models (Llama, Qwen, Mistral) via Databricks, vLLM, or similar, with a point of view on self-host vs. hosted-API cost/latency trade-offs.</span></li></ul><h2><br></h2><h3><strong style="background-color: transparent; color: rgb(0, 0, 0);">UP.Labs Summary</strong></h3><div><span style="background-color: transparent; color: rgb(0, 0, 0);">We build high-growth technology startups that enable faster, cleaner, and safer movement of people and goods. Our vision is to transform the moving world by pairing leading corporations and entrepreneurs with a proven methodology for launching and scaling software and hardware companies.</span></div><div><span style="background-color: transparent; color: rgb(0, 0, 0);">We work with corporate investors over a multi-year period to launch a portfolio of mobility-focused ventures. Our team is dedicated to the first year of a new venture's life cycle, from ideation to minimum viable product build (and beyond) to recruiting and hiring the full-time team who will scale the business.</span></div><h3><br></h3><h3><strong style="background-color: transparent; color: rgb(0, 0, 0);">Location</strong><span style="background-color: transparent; color: rgb(0, 0, 0);">: Remote</span></h3><h2 style="text-align: justify;"><br></h2>