Agentic AI Engineer

Responsibilities

• Design and implement enterprise-grade AI agents capable of reasoning, planning, decision-making, and autonomous task execution.
• Develop multi-agent systems where specialized agents collaborate, exchange information, delegate tasks, and validate each other’s outputs.
• Build scalable agent architectures using Agent Development Kit (ADK), Google Cloud Vertex AI, Gemini models, Agent Engine, and related Google AI services.
• Integrate AI agents with enterprise applications, APIs, databases, RPA platforms, and internal business systems.
• Design Retrieval-Augmented Generation (RAG) solutions using vector databases, semantic search, embeddings, and knowledge repositories.
• Implement context management strategies including conversational memory, long-term memory, retrieval mechanisms, and context optimization.
• Develop secure agent workflows with authentication, authorization, encryption, audit logging, and enterprise governance controls.
• Build tool integrations using REST APIs, MCP-compatible tools, databases, messaging platforms, and external services.
• Develop Python-based services, utilities, and integrations supporting AI agent capabilities.
• Monitor, evaluate, and optimize AI agents for quality, latency, cost efficiency, reliability, and business impact.

Languages