AI Engineering Operations Lead (Azure DevOps & GenAI)

ICONMA is an IT Services and Consultant company seeking an AI Engineering Operations Lead to oversee engineering operational execution within the Software Development Engineering organization. The role involves maintaining Azure DevOps backlogs, ensuring alignment with SDLC requirements, and driving the adoption of AI-enabled engineering practices.

Responsibilities:

• Own engineering operational execution within the Software Development Engineering organization
• Maintain Azure DevOps backlogs, work item structures, workflows, dependencies, traceability, and engineering documentation required to support delivery execution
• Ensure MVPs, Epics, Features, User Stories, Technical Tasks, Defects, and engineering work items are complete, synchronized, and aligned with SDLC and governance requirements
• Partner with Product Managers, Business Analysts, Architects, and technical leads to ensure requirements, specifications, acceptance criteria, dependencies, and supporting artifacts are development-ready before each iteration
• Support engineering teams by managing operational activities that would otherwise consume developer capacity
• Ensure engineering metrics, delivery data, traceability information, and backlog health indicators are accurately captured, maintained, and validated within engineering systems of record
• Provide complete and reliable delivery data that enables Agile PMO and leadership teams to perform stakeholder reporting, governance reviews, and portfolio oversight activities
• Coordinate with Scrum Masters and Agile PMO to maintain alignment between engineering execution, planning activities, and governance requirements
• Manage engineering work item hygiene, documentation quality, dependency tracking, release readiness, and audit traceability
• Develop AI-powered agents and automation solutions to reduce manual administration, improve data quality, and streamline engineering workflows
• Leverage AI tools to automate documentation updates, backlog maintenance, traceability management, engineering reporting inputs, and governance artifacts
• Continuously identify opportunities to eliminate non-value-added engineering activities through workflow simplification and automation
• Drive adoption of AI-enabled engineering operations practices while remaining aligned with Agile, SDLC, compliance, and governance standards
• Establish repeatable operational processes that improve backlog quality, development readiness, traceability, engineering productivity, and delivery predictability
• Contribute to documentation of engineering standards, operating procedures, governance controls, and automation frameworks
• Act as the operational extension of the software engineering organization, enabling engineers to focus on coding, architecture, testing, code reviews, and innovation

Requirements:

• 10+ years of experience in software delivery, engineering operations, application development lifecycle management, technical program management, or related engineering support functions
• Deep experience with Azure DevOps, including Boards, Backlogs, Work Item Management, Queries, Dashboards, and workflow administration
• Strong understanding of Agile delivery, SDLC processes, software engineering practices, and release management
• Experience working closely with Product Managers, Business Analysts, Architects, Developers, QA teams, and business stakeholders
• Experience managing engineering operational processes, backlog governance, traceability, and delivery data quality
• Strong understanding of requirements management, acceptance criteria, dependency management, and development readiness practices
• Experience designing and implementing workflow automation solutions that improve engineering productivity and reduce manual effort
• Strong analytical, organizational, communication, and stakeholder management skills
• Experience operating within enterprise-scale technology organizations with established governance and compliance requirements
• Familiarity with AI-assisted workflows, AI agents, engineering productivity tools, and GenAI platforms
• Demonstrated ability to leverage AI and automation to improve operational efficiency, documentation quality, reporting accuracy, and engineering effectiveness
• Azure DevOps for backlog management, work item administration, traceability, and engineering operations
• Microsoft Copilot, GitHub Copilot, Claude, ChatGPT, or similar AI platforms
• Power Automate, Copilot Studio, Logic Apps, and other workflow automation technologies
• Power BI and dashboarding platforms for engineering operational analytics
• Jira, Confluence, SharePoint, and enterprise collaboration platforms
• SQL, Excel, and reporting tools for delivery insights and operational analysis
• Microsoft Teams and related collaboration technologies