Agentic AI Engineer

·Build and support production-grade Agentic AI solutions using cloud-native AI platforms such as AWS Bedrock, Azure AI Foundry, or Google Cloud Platform Vertex AI.

·Design intelligent agents capable of tool use, workflow orchestration, retrieval-augmented generation, reasoning, task automation, memory management, and multi-agent collaboration.

· Apply strong Data engineering experience to create scalable APIs, data pipelines, integrations, agent tools, and AI-enabled applications.

·Enable secure and interoperable agent ecosystems using standards and protocols such as MCP and A2A for tool connectivity, context access, and agent-to-agent communication.

·Design, develop, and maintain Agentic AI applications using AWS Bedrock, Azure AI Foundry, or Google Cloud Platform Vertex AI.

·Build AI agents that can perform planning, reasoning, tool calling, memory management, workflow automation, and multi-step task execution.

·Implement integrations using Model Context Protocol — MCP to enable agents and LLM applications to securely connect with external tools, APIs, enterprise systems, databases, and data sources.

·Work with Agent2Agent — A2A patterns or protocols to support agent discovery, agent-to-agent communication, task delegation, and multi-agent collaboration.

· Develop backend services, APIs, connectors, and integrations to enable AI agents to interact with enterprise applications, databases, documents, and external systems.

· Implement Retrieval-Augmented Generation solutions using vector databases, embeddings, document processing, semantic search, and knowledge retrieval patterns. GraphRAG experience is a plus.

·Work with LLM frameworks and orchestration tools such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, AutoGen, CrewAI, Strands or equivalent technologies.

·Evaluate, test, and improve agent performance, including prompt quality, response accuracy, latency, reliability, guardrails, security, and cost efficiency.

·Debug and resolve issues related to agent behavior, API integrations, tool execution, MCP servers or clients, A2A communication flows, data retrieval, workflow failures, model responses, and production deployments.

·Collaborate with architects, product owners, backend developers, data engineers, and DevOps teams while following best practices for testing, CI/CD, monitoring, documentation, and secure deployment