Agentic AI Engineer

The Agentic AI Engineer will design, build and deploy AI agents that automate multi-step business processes across enterprise systems such as ERP, CRM, email and collaboration tools.

The role requires hands-on engineering with LLMs and agent tooling, a practical understanding of what makes software enterprise-ready, and the ability to work with business users, functional consultants and junior engineers.

Candidates from software engineering, integration or automation backgrounds (RPA, low-code workflows) who have built LLM-based agents, at work or on their own, are also encouraged to apply.

Key Responsibilities

• Design, build and deploy AI agents, tools and skills that take actions in business systems, not only answer questions.
• Translate business processes into agent workflows with clear steps, decision points, approvals and exception paths.
• Build long-running workflows that pause for human input, resume reliably and avoid duplicate transactions.
• Design human-in-the-loop steps over email and chat where users confirm or correct agent-proposed values instead of answering open questions.
• Integrate agents with enterprise applications through APIs, MCP servers and existing integration layers.
• Apply access control, least-privilege service identities and segregation of duties, in line with customer security and data privacy standards.
• Build audit logging, monitoring and evaluation so every agent action is traceable and measurable.
• Handle errors, retries and edge cases, and route failures to the right person with enough context to act.
• Work with functional consultants and business users to capture requirements and test with realistic scenarios.
• Write documentation and runbooks, demo working solutions to stakeholders, and mentor junior engineers.

Job Requirements

Must-have

• 2 to 3 years of hands-on experience in software, integration or automation engineering.
• Built at least one LLM-based agent or automation, at work or on your own, that uses tool or function calling to take real actions in a business system.
• Hands-on experience with at least one LLM platform such as OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock or Google Vertex AI.
• Experience with at least one agent framework such as LangChain / LangGraph, LlamaIndex, Microsoft Agent Framework / Semantic Kernel, CrewAI or OpenAI Agents SDK.
• Strong Python or TypeScript / JavaScript, with REST APIs, JSON and authentication (OAuth 2.0, service accounts).
• Hands-on experience with at least one workflow or orchestration tool such as Microsoft Power Automate, Azure Logic Apps, n8n, UiPath or Camunda.
• Working knowledge of Git, CI/CD, Docker and testing practices.
• Understanding of enterprise requirements: access control, audit trails, error handling and data privacy.
• Strong analytical, problem-solving and communication skills, with the ability to explain technical ideas to business users.

Nice-to-have

• Exposure to SAP or other ERP processes such as procurement, finance or supply chain.
• Experience building or using Model Context Protocol (MCP) servers or the Agent2Agent (A2A) protocol.
• Integration with enterprise applications such as Salesforce, Dynamics 365 or ServiceNow.
• Microsoft 365 integration: Teams bots, adaptive cards, Outlook automation or the Microsoft Graph API.
• Experience on AWS, Azure or Google Cloud (serverless functions, storage, identity and access management); a cloud certification is a plus.
• Retrieval-augmented generation (RAG) with vector stores such as pgvector, OpenSearch or Pinecone.
• Event-driven architecture using Kafka, queues or webhooks.
• LLM observability and evaluation tools such as LangSmith, Langfuse or OpenTelemetry.
• Experience mentoring junior engineers.

What We Offer

• High-Impact Role: Opportunity to help build Accel4’s Agentic AI practice and take agent solutions from pilot to production.
• Customer Exposure: Direct interaction with business and technology stakeholders across North American enterprise customers.
• End-to-End Ownership: Opportunity to own agent solutions from requirements and design through testing and go-live.
• Technology Exposure: Work across LLM platforms, agent frameworks, enterprise applications, integration and cloud technologies.
• Entrepreneurial Environment: Work closely with leadership and play a meaningful role in shaping a growing technology practice.