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Staff System Integration Engineer Jobs (NOW HIRING)

Prosum Inc.

Systems Integration Engineer

Phoenix, AZ · On-site

$205K/yr

System Integration Engineer Our client is seeking a System Integration Engineer for a direct hire opportunity in North Phoenix, AZ. This will be a heavy travel role of 30-40% but they would like the ...

Socket.dev

Camera System Integration Engineer

Cupertino, CA · On-site

$150 - $200/hr

We are searching for extraordinary Hardware Engineers to take ownership in the development and system integration of very high-performance, compact camera modules and depth-sensing hardware such as ...

Apple

Camera System Integration Engineer

Cupertino, CA · On-site

$206K/yr

We are searching for extraordinary Hardware Engineers to take ownership in the development and system integration of very high-performance, compact camera modules and depth-sensing hardware such as ...

V2X

System Integration Engineer IV

Boston, MA · On-site

$181K/yr

The Systems Integration Engineer will be responsible for leading a small team in the design, test ... This person will mentor less experienced staff members in project management techniques, written ...

V2X

System Integration Engineer IV

Boston, MA · On-site

$181K/yr

The Systems Integration Engineer will be responsible for leading a small team in the design, test ... This person will mentor less experienced staff members in project management techniques, written ...

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Staff System Integration Engineer information

See salary details

$65K

$157K

$167.5K

How much do staff system integration engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for staff system integration engineer in the United States is $157,042.00, according to ZipRecruiter salary data. Most workers in this role earn between $167,000.00 and $167,000.00 per year, depending on experience, location, and employer.

What is a staff system integration engineer?

Staff System Integration Engineers are experienced professionals responsible for designing, implementing, and overseeing the integration of complex hardware and software systems within an organization. They coordinate between various engineering teams to ensure all subsystems work together seamlessly, troubleshoot integration issues, and develop strategies for system optimization. Their role often involves setting technical standards, mentoring junior engineers, and leading large-scale integration projects to meet business and technical requirements.

What are some common challenges faced by staff system integration engineers, and how can they be addressed?

Staff System Integration Engineers often encounter challenges related to coordinating between diverse hardware and software components, as well as aligning integration efforts across cross-functional teams. Issues such as compatibility, timing, and unforeseen system interactions are typical. To address these, it’s important to maintain clear documentation, conduct thorough testing, and foster strong communication with stakeholders from hardware, software, and QA teams. Proactively identifying integration risks early in the development cycle and leveraging automation tools for continuous integration can also help streamline the process.

What are the key skills and qualifications needed to thrive as a staff system integration engineer, and why are they important?

To thrive as a Staff System Integration Engineer, you need a strong background in systems engineering, integration testing, and troubleshooting, typically supported by a degree in engineering or computer science. Expertise in integration tools (such as Jenkins, Docker, or Kubernetes), scripting languages, and familiarity with hardware-software interfaces are commonly required, along with relevant certifications like INCOSE. Excellent problem-solving, communication, and project management skills help you collaborate with cross-functional teams and manage complex integration challenges. These competencies ensure seamless system functionality, reduce integration risks, and drive successful project outcomes.

What is the difference between Staff System Integration Engineer vs System Integration Engineer?

AspectStaff System Integration EngineerSystem Integration Engineer
CredentialsBachelor's or higher in Engineering, certifications like PMP or CiscoBachelor's in Engineering or related field, certifications vary
Work EnvironmentLarge tech companies, enterprise projects, cross-functional teamsVaried industries, project-based, both corporate and consulting settings
Employer & Industry UsageUsed in tech, telecommunications, and manufacturing sectorsCommon across IT, telecom, and industrial sectors
Search & Comparison IntentOften compared for seniority, responsibilities, and scopeCompared for entry-level to mid-level roles, scope of work

The Staff System Integration Engineer typically holds more experience, handles complex projects, and may lead teams, whereas the System Integration Engineer focuses on implementing and testing integrations. Both roles require similar technical skills and certifications but differ mainly in scope and seniority.

AI SYSTEM INTEGRATION ENGINEER

Chicago, IL • On-site

Judge Group, Inc.
Recruiting and Staffing Services • 5 - 10K employees
Learn more about Judge Group, Inc.external

$80 - $100/hr

Other

Posted 22 days ago

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.

  • Support Retrieval-Augmented Generation (RAG) and enterprise knowledge management solutions by integrating structured and unstructured data sources.

Job description

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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