Xerxes Global

AI Engineer

• Create agentic systems using frameworks like LangGraph and LangChain, implementing tool-calling, planning, and self-correction capabilities
• Design and implement sophisticated retrieval-augmented generation pipelines with multi-modal capabilities, handling text, tables, and complex document structures
• Deploy and monitor AI systems using Docker, Kubernetes, and CI/CD pipelines, ensuring reliability, scalability, and cost-effectiveness

• Create agentic systems using frameworks like LangGraph and LangChain, implementing tool-calling, planning, and self-correction capabilities
• Design and implement sophisticated retrieval-augmented generation pipelines with multi-modal capabilities
• Deploy and monitor AI systems using Docker, Kubernetes, and CI/CD pipelines, ensuring reliability, scalability, and cost-effectiveness

• Lead the design, development, and production deployment of autonomous multi-agent systems
• Design multi-agent architectures capable of breaking down complex user queries
• Define state management strategy for agents
• Architect Retrieval-Augmented Generation (RAG) pipelines
• Implement tool-use capabilities for agents
• Develop guardrails and steering mechanisms for agents
• Oversee transition from prototype to production
• Implement evaluation frameworks to measure agent performance
• Design observability dashboards for real-time tracing
• Manage cost and performance trade-offs in model selection

AI Architect

• Lead the design, development, and production deployment of autonomous multi-agent systems
• Design multi-agent architectures capable of breaking down complex user queries into sub-tasks
• Implement tool-use capabilities enabling agents to interact with internal APIs, databases, and third-party SaaS safely
• Oversee the transition from prototype to production, ensuring code is modular, testable, and scalable
• Implement evaluation frameworks to measure agent performance, accuracy, and hallucination rates