Lead Platform Engineer

As the Lead AI Platform Engineer at our organization, you will play a crucial role in architecting and implementing the foundational elements of our Agentic AI Platform. Your responsibilities will include designing, deploying, and maintaining production-grade multi-agent systems leveraging frameworks like CrewAI, LangGraph, or custom-built orchestration layers. You will collaborate closely with data engineers, data scientists, ML engineers, and product teams to create adaptive, reasoning-driven systems. - Architect & Build Agentic Systems: Design, develop, and deploy multi-agent workflows using frameworks such as CrewAI, LangGraph, or custom-built orchestration layers. - Platform Engineering: Construct the agentic runtime, including message routing, memory management, and context-sharing mechanisms between agents. - LLM & Tool Integration: Integrate Large Language Models, vector databases, retrieval systems, and external APIs for agent tool-use and reasoning capabilities. - Workflow Design & Optimization: Collaborate with AI researchers and solution engineers to design dynamic agent workflows that adapt based on contextual information and analytical results. - Cloud & Scalability: Architect scalable deployments on AWS, GCP, or Azure, leveraging cloud-native components for high availability and performance. - Observability & Governance: Implement monitoring, evaluation metrics, and safety checks for autonomous agents to ensure reliability and compliance. - Team Leadership & Mentorship: Provide guidance to a team of engineers, establish best practices, and build high-performance engineering culture. Required Qualifications: - Strong background in Python with experience in asynchronous programming, API development, and distributed systems. - Proven experience in building LLM-powered multi-agent systems using frameworks like CrewAI, LangGraph, or similar. - Deep understanding of prompt engineering, RAG pipelines, and tool-calling mechanisms. - Hands-on experience with cloud infrastructure and MLOps components. - Solid understanding of state management, context persistence, and memory architecture for agents. - Experience integrating vector stores and LLM APIs. - Ability to architect scalable AI systems from prototype to production. - Excellent communication and collaboration skills. - Experience with Graph-based orchestration or LangGraph advanced workflows. - Familiarity with agent coordination frameworks like CrewAI. Nice-to-Have: - Exposure to event-driven architectures, message queues, or knowledge graphs. - Understanding of AI safety, alignment, and governance principles. - Contributions to open-source agent frameworks or AI orchestration tools. - Ability to customize models to specific purposes. In addition to working on cutting-edge technology and being part of a collaborative, innovation-driven culture, we offer competitive compensation, potential equity options, and the opportunity to build the next-generation Agentic AI Platform with real-world impact.,