AI Engineer (Remote, LATAM)

Overview:

Technical Challenge:

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).
• Familiarity with Databricks, including experience working with its data engineering and analytics capabilities.
• 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.
• Expertise in containerized deployment with Docker.
• 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:

Location: Remote