Agentic AI Engineer

Reuben Cooley Inc.; New York (NY); On-site; USD 120,000 - 190,000; Full time

Job summary

Reuben Cooley Inc. seeks an Agentic AI Engineer to design, build, and deploy agent‑based systems using cloud platforms (AWS Bedrock, Azure AI Foundry, GCP Vertex AI). The role emphasizes scalable back‑end services and AI‑enabled workflows.

You will collaborate with architects, data engineers, and developers to enable tool use, multi‑agent coordination, and secure integrations in production environments.

Qualifications

• 1–2 years of hands‑on Agentic AI experience
• 5+ years of backend development or data engineering
• Experience with cloud‑native AI platforms
• Strong system design and API skills
• Ability to collaborate with cross‑functional teams

Responsibilities

• Build production‑grade Agentic AI solutions on cloud platforms
• Design agents with tool use, orchestration, RAG, and multi‑agent work flows
• Develop scalable APIs, data pipelines, integrations and tools
• Implement MCP/A2A protocols for secure tool connectivity
• Collaborate with architects, data engineers, and developers in production deployments

Job description

We are seeking an Agentic AI Engineer with hands‑on experience in building AI agents, LLM‑powered applications, and intelligent workflow automation solutions using platforms such as AWS Bedrock, Azure AI Foundry, or GCP Vertex AI. The ideal candidate should have 1–2 years of practical experience in Agentic AI and a strong foundation of at least 5 years in backend development or data engineering. This role will focus on designing, developing, and integrating agent‑based systems that can reason, plan, retrieve information, use tools, communicate with other agents, and automate complex business workflows. The candidate will work closely with architects, product teams, data engineers, and application developers to deliver scalable, secure, interoperable, and production‑ready AI solutions

Roles And 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 backend development or 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