Senior Machine Learning Engineer – LLMs, Agentic Workflows

This is a fully remote position, open to applicants in Latin America.

📋 Description

• Architect the decomposition of agent tasks into actionable sub-tasks

• Develop Plan-and-Execute and ReAct methodologies

• Design short-term memory systems tailored for long-running tasks

• Create interfaces that connect LLMs with external software, databases, and APIs

• Standardize function calls, interactions with legacy systems, and sandboxed Python execution

• Implement mechanisms for error handling and self-correction

• Build evaluation frameworks to measure trajectory success, efficiency, and safety

• Set up monitoring systems to visualize agent processes and diagnose workflow failures

• Implement guardrails and Human-in-the-Loop checkpoints for critical decisions

• Oversee intelligent automation systems from initial prototyping to final production deployment

⛳️ Requirements

• A minimum of 5 years of practical experience in developing and deploying machine learning models in production settings

• Strong fundamentals in software engineering, including knowledge of data structures, algorithms, system design, object-oriented programming, and API design & integration

• Proven background in designing and implementing agentic architectures, including multi-agent workflows, tool-calling, state management, and human-in-the-loop patterns

• Expertise in integrating Generative AI frameworks and APIs such as LangChain, LangGraph, OpenAI, and Claude into production-grade applications

• Solid understanding of LLM fundamentals, systematic prompt engineering (chain-of-thought, few-shot), and debugging tools like LangSmith or Arize Phoenix

• Experience with vector databases like Pinecone, Milvus, and Qdrant for retrieval-augmented generation (RAG) and long-term agent memory

• Familiarity with cloud platforms such as AWS, GCP, or Azure for the deployment of AI workloads

🏝️ Benefits

• Ownership through equity participation

• Annual company retreat

• Education bonus for ongoing learning

• Company-wide winter break

• Paid time off

• Optional in-person events and meetups

• Customized career roadmaps

• High-performance culture