Agentic AI Engineer

HireOn Tech is seeking an Agentic AI Engineer to build and integrate production-ready AI agents and LLM-powered applications. The role focuses on intelligent workflow automation, agent interoperability, and scalable backend and data engineering solutions.

Responsibilities:

• Build and support production-grade Agentic AI solutions using cloud-native AI platforms such as AWS Bedrock, Azure AI Foundry, or GCP 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 GCP 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