Sr AI/ML Engineer (LATAM Remote)

Overview UPLabs 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 AI powered solutions for enterprise customers. Technical Challenge You will design, build, and iterate on applied AI agentic workflows and machine learning solutions, with a strong focus on natural language processing use cases, to support AI powered solution creation and product delivery. You will work across the modeling and experimentation lifecycle, leveraging Python and modern AI frameworks, and collaborate to deploy and operationalize solutions in Databricks and cloud provider environments. Responsibilities Lead end-to-end machine learning initiatives—from problem framing and data exploration through model development, evaluation, and iterative improvement—focused on NLP-heavy applications. Develop and maintain Python-based ML pipelines and experimentation workflows to support rapid iteration. Build and integrate Generative AI solutions using modern AI frameworks (e.g., LangChain, CrewAI), ensuring solutions are testable and maintainable. Define evaluation criteria and run structured model/LLM evaluation [... source excerpt omitted ...] scale experimentation and training workflows. Required Skills Strong applied experience in data science and machine learning, including developing, evaluating, and iterating on production-oriented models. Demonstrated ability to build AI/ML solutions using Python. Hands-on experience with modern AI application frameworks such as LangChain or CrewAI. Practical experience working with Generative AI techniques and patterns in real-world applications. Experience working with graph based solutions (e.g. Neo4j) to design and implement ontologies and context graphs for AI agents. Understanding of agentic harnesses development including memory management and dynamic context compres