GenAI Engineer (aOS)

About the Role 

We are seeking a talented and motivated Agent OS Developer to join our team in designing and building the core infrastructure that powers intelligent agent-based systems. You will be instrumental in creating the operating system that orchestrates agent behaviors, manages workflows, and integrates with external tools and APIs. This is a unique opportunity to shape the next generation of computing where autonomous agents collaborate to solve complex tasks. 

Key Responsibilities 

• Design and develop the Agent Operating System (Agent OS) to support multi-agent coordination, task planning, and execution. 

• Implement core services such as memory, perception, planning, scheduling, environment modeling, and long-term state persistence. 

• Develop abstractions and APIs that allow developers to define, manage, and deploy agents in dynamic environments. 

• Integrate with LLMs, vector stores, toolchains, and third-party APIs to enable agent capabilities such as reasoning, code generation, and data analysis. 

• Build agent lifecycle management tools, including initialization, state management, failure recovery, and inter-agent communication. 

• Ensure the Agent OS is secure, scalable, fault-tolerant, and optimized for low-latency interactions. 

• Collaborate with research, ML, and platform teams to test and iterate on features grounded in real-world use cases. 

• Contribute to documentation, developer tooling, and best practices for building with Agent OS. 

Preferred Qualifications 

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field. 

• 4+ years of experience in systems-level development, distributed systems, or AI frameworks. 

• Proficiency in Python, Rust, or Go. Experience with asynchronous programming and microservices architecture. 

• Familiarity with agentic frameworks like LangChain, LangGraph, CrewAI, Autogen, ReAct, or bespoke task-oriented agents. 

• Familiarity with patterns like Supervisor, Judge and multi-agent architecture 

• Deep understanding of AI/ML infrastructure, including LLMs, embeddings, prompt orchestration, and vector databases. 

• Experience with multi-agent systems, behavior trees, or reinforcement learning is a plus. 

• Knowledge of containerization (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure). 

Nice to Have 

• Contributions to open-source agent frameworks or AI tooling. 

• Experience working with observability and monitoring tools to track agent performance. 

• Exposure to knowledge graphs, memory management systems, or retrieval-augmented generation (RAG) pipelines.