AI/ML Engineer — Multi-Agent Systems
About the Role
Design multi-agent coordination systems, memory retrieval pipelines, and reasoning workflows that power Zaby's Agent Squad and Agentic Workflows products.
As part of the Zaby Engineering team, you will be at the forefront of the autonomous enterprise revolution. We are building the critical infrastructure that allows AI agents to move beyond static interaction and into reliable, persistent execution within complex enterprise environments.
Responsibilities
Design and implement coordination protocols for multi-agent systems to solve complex, multi-step tasks.
Develop advanced RAG (Retrieval-Augmented Generation) pipelines for agent memory and context persistence.
Fine-tune and optimize LLMs for specific operational tasks and reasoning workflows.
Research and implement evaluation frameworks for agent performance and reliability.
Work closely with product teams to translate user requirements into agentic capabilities.
Requirements
3+ years of experience in AI/ML engineering with a focus on NLP or multi-agent systems.
Strong proficiency in Python and deep learning frameworks (PyTorch, JAX).
Experience with LLM orchestration (LangChain, AutoGen, etc.) and prompt engineering.
Solid understanding of vector storage and semantic retrieval architectures.
Master's or PhD in CS/AI or equivalent industry experience.
Bonus Points
Experience with reinforcement learning or autonomous systems.
Published research in top-tier AI conferences.
Experience deploying large-scale ML models in production environments.