AI SYSTEM INTEGRATION ENGINEER

Location: Chicago, IL Salary: $80.00 USD Hourly - $100.00 USD Hourly Description: Title: AI System Integration Engineer

Job Summary

We are seeking a Senior AI Integration Engineer to design, develop, and support enterprise-grade integrations that connect AI-powered digital coworkers, Generative AI platforms, business applications, data sources, and workflow systems. The ideal candidate will bring strong expertise in cloud-native engineering, enterprise integrations, AI technologies, and production platform support.

This role will collaborate closely with Product, Engineering, Architecture, Security, Risk, and Business stakeholders to build scalable, secure, and reusable integration patterns that accelerate AI adoption across the organization.

Key Responsibilities

• Design, develop, test, deploy, and maintain integrations between AI solutions, enterprise applications, APIs, workflow platforms, and data sources.
• Build reusable services, connectors, and integration frameworks that support multiple AI and digital coworker use cases.
• Develop and implement API-driven, event-driven, and microservices-based integration architectures.
• Support Retrieval-Augmented Generation (RAG) and enterprise knowledge management solutions by integrating structured and unstructured data sources.
• Configure workflow orchestration, permissions, tool access, and governance controls for AI agents and digital coworkers.
• Collaborate with Product, Security, Risk, Architecture, and Application teams to validate technical feasibility, dependencies, access requirements, and implementation strategies.
• Troubleshoot production issues, perform root cause analysis, and implement sustainable remediation solutions.
• Contribute to sprint planning, backlog refinement, release management, dependency tracking, and project status reporting.
• Create and maintain technical documentation, interface specifications, test plans, deployment guides, operational runbooks, and support materials.
• Continuously improve integration standards, monitoring capabilities, security controls, and delivery processes.

Required Qualifications

• 5+ years of experience in Software Engineering, Integration Engineering, Platform Engineering, Cloud Engineering, or related technology roles.
• Proven experience designing and implementing enterprise-scale integrations using APIs, microservices, event-driven architectures, and service-based platforms.
• Hands-on experience with cloud-native development and deployment, preferably on Microsoft Azure.
• Strong experience supporting production platforms, including monitoring, troubleshooting, incident management, and root cause analysis.
• Understanding of enterprise security principles including authentication, authorization, identity management, data protection, logging, and audit controls.
• Experience translating business requirements into practical technical solutions and architecture designs.
• Experience working within Agile delivery environments using tools such as Azure DevOps, Jira, ServiceNow, Confluence, and SharePoint.
• Strong communication, stakeholder management, and technical documentation skills.

Preferred Qualifications

• Experience working in financial services, regulated industries, enterprise governance, risk management, or AI compliance environments.
• Working knowledge of:
• Generative AI
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• AI Agents and Agentic Workflows
• Prompt Engineering
• Responsible AI and AI Governance
• AI Observability and Model Monitoring
• Experience with:
• Azure OpenAI
• Azure API Management
• Azure Functions
• Azure Kubernetes Service (AKS)
• Microsoft Graph
• Copilot Studio
• LangChain
• LangGraph
• Model Context Protocol (MCP)
• Experience integrating enterprise platforms such as SharePoint, ServiceNow, Snowflake, Databricks, Microsoft Fabric, document repositories, and operational systems.
• Experience creating reusable connector frameworks, deployment documentation, test strategies, release notes, and operational runbooks.

Required Technical Skills

AI & Agentic Platforms

• Generative AI and LLM concepts
• AI Agents and Agentic Frameworks
• RAG and Hybrid RAG architectures
• Prompt Engineering
• MCP (Model Context Protocol)
• Responsible AI and Governance Controls
• AI Observability, Monitoring, and Telemetry
• LangChain and LangGraph

Integration Engineering

• REST APIs
• Service-Oriented Architecture
• Event-Driven Architecture
• Microservices Design
• API Gateway Management
• Authentication and Authorization Patterns
• Integration Testing
• Error Handling and Retry Mechanisms
• Reusable Connector Development

Cloud & DevOps

• Microsoft Azure
• Azure Functions
• Azure API Management (APIM)
• Azure Kubernetes Service (AKS)
• Azure DevOps
• CI/CD Pipelines
• Infrastructure-as-Code Concepts
• Monitoring and Logging
• Release and Environment Management

Data & Analytics

• Python
• SQL
• Kafka
• Snowflake
• Databricks
• Microsoft Fabric
• Structured and Unstructured Data Integration
• Knowledge Repository Management
• Data Access Controls
• Retrieval Optimization

Tools & Reporting

• Azure DevOps
• Jira
• ServiceNow
• Confluence
• SharePoint
• Power BI
• Excel
• Technical Documentation Platforms
• Operational Runbooks and Dashboards

Enterprise Delivery

• Cross-functional Collaboration
• Dependency Management
• Security and Risk Review Support
• Governance Documentation
• Vendor Coordination
• Production Support Readiness
• Release Planning and Change Management

Success Measures

• Delivery of secure, scalable, and well-documented enterprise integrations.
• Development of reusable integration and connector frameworks that improve engineering efficiency.
• Faster onboarding of AI solutions and digital coworkers into enterprise systems and data platforms.
• Reduction in production defects and improvement in incident response readiness.
• High-quality technical documentation, operational support assets, and governance compliance artifacts.

Technical Skills (Mandatory)

• LangChain / LangGraph Tool Stack
• Python
• Kafka
• Snowflake
• Databricks
• Azure Cloud Services
• REST APIs & Integration Engineering
• Generative AI & RAG Frameworks
• MCP and AI Agent Frameworks
• Azure DevOps & CI/CD Pipelines

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