AI Engineer (Remote, LATAM)

Overview: 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 skilled AI Agent Engineer to join our growing team and contribute to our mission of launching the next wave of successful startups. Technical Challenge: As an AI Agent Engineer at UP.Labs, you will design, implement, and deploy agentic AI workflows — systems where LLMs orchestrate multi-step reasoning, tool use, and decision-making — to power real-time tooling across manufacturing, logistics, and supply chain domains. You will be responsible for building solutions that behave predictably and produce near-deterministic outputs in production environments. This is a hands-on role requiring strong technical expertise, creativity, and a passion for innovation in the transportation industry. In this role you will: Design, build, and deploy agentic workflows (multi-step LLM chains with tool calling, retrieval, and structured output) for real-time, business-critical use cases. Engineer for determinism and consistency by implementing constrained decoding, structured outputs, caching layers, and evaluation harnesses. Build and maintain evaluation and regression frameworks — automated pipelines that measure accuracy, latency, and behavioral consistency across prompt and model changes. Integrate LLM agents with external tools and APIs (databases, rules engines, business systems) using frameworks like LangFuse, LangChain, LangGraph, CrewAI, or custom orchestration. Deploy agentic systems on cloud infrastructure (AWS, Azure, and/or GCP), optimizing for low-latency inference and cost efficiency. Implement guardrails, fallback logic, and observability to ensure agents fail gracefully and every decision is traceable. Collaborate with data scientists, software engineers, and business stakeholders to translate business rules into agent behavior and tool definitions. Stay current with the latest advancements in AI agents, large language models, and cloud technologies. Required Skills: Practical, hands-on experience building and deploying agentic AI systems in production environments. Proficiency in Python and experience building production backend systems. Experience with LLM APIs (OpenAI, Anthropic, etc.) and agentic frameworks (LangFuse, LangChain, LangGraph, CrewAI, AutoGen, or equivalent). Strong understanding of prompt engineering for reliability: structured outputs, few-shot patterns, chain-of-thought, and techniques that minimize hallucination. Experience building evaluation and testing pipelines for AI systems, including behavioral evals and golden-set testing. Expertise in at least one major cloud provider (AWS, Azure, and/or GCP) and containerized deployment (Docker, Kubernetes). Familiarity with vector databases (Pinecone, Weaviate, pgvector) and retrieval-augmented generation (RAG) patterns. Solid knowledge of version control systems (e.g., Git) and CI/CD pipelines. Strong problem-solving skills and ability to work collaboratively across teams. Preferred Expertise: Advanced degree (Master's or PhD) in Computer Science, Machine Learning, or a related field. Experience building systems where AI outputs feed directly into business-critical decisions. Experience in the transportation and logistics industry. Familiarity with MLOps/LLMOps tooling. Experience with fine-tuning or distillation to optimize for speed and cost at inference time. Knowledge of rules engines or constraint solvers and how to combine them with LLM reasoning. UP.Labs Summary : 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. 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. Location: Remote