AI Agent Orchestration Lead

Key Responsibilities

• AI Agent Development & Orchestration
• Design, develop, and operationalize AI agents that automate software development, testing, deployment, documentation, and operational support activities.
• Build scalable AI orchestration frameworks with clear ownership, lifecycle management, and failure recovery mechanisms.
• Standardize AI agent templates, prompts, orchestration models, and automation patterns across engineering teams.
• Consolidate fragmented AI initiatives into centrally governed enterprise solutions.
• SDLC & DevOps Integration
• Integrate AI agents into enterprise SDLC workflows to improve development speed, software quality, and operational efficiency.
• Embed AI capabilities within DevOps ecosystems, including CI/CD pipelines, testing platforms, backlog management tools, and ITSM solutions.
• Integrate AI workflows with observability platforms, monitoring systems, and enterprise knowledge management tools.
• Identify and prioritize high-value automation opportunities across the software delivery lifecycle.
• Governance, Compliance & Risk Management
• Implement human-in-the-loop controls, approval workflows, escalation mechanisms, and audit capabilities.
• Ensure AI outputs are traceable, compliant, actionable, and aligned with business requirements.
• Implement responsible AI practices, governance frameworks, compliance standards, and operational controls.
• Collaborate with Enterprise Architects, Security Teams, Risk Teams, and Platform Engineers to maintain architectural alignment and regulatory compliance.
• Monitoring & Platform Operations
• Establish monitoring, telemetry, performance analytics, and reporting frameworks for AI agent ecosystems.
• Support AI platform lifecycle management, scalability planning, and operational reliability initiatives.
• Drive continuous improvement efforts to enhance platform performance and operational maturity.
• Develop operational playbooks, implementation frameworks, and technical documentation.
• Leadership & Stakeholder Engagement
• Drive adoption of AI-powered workflows and best practices across engineering and delivery teams.
• Mentor technical teams on AI orchestration, automation strategies, and engineering excellence.
• Partner with business and technology stakeholders to define success metrics and measure outcomes.
• Deliver executive-level updates focused on adoption, operational performance, risk management, and business value realization.
• Evaluate emerging AI technologies, orchestration platforms, and automation frameworks to support innovation initiatives.

Key Skills

• AI Agent Development, Agent Orchestration, and Enterprise Automation
• Generative AI, Large Language Models (LLMs), and Intelligent Workflows
• Software Development Life Cycle (SDLC), DevOps Practices, and Platform Engineering
• Enterprise Architecture, Solution Design, and Technical Governance
• CI/CD Pipeline Integration, Delivery Automation, and Workflow Optimization
• AI Governance, Responsible AI Practices, and Compliance Frameworks
• Human-in-the-Loop Systems, Approval Mechanisms, and Audit Controls
• Workflow Automation, Process Transformation, and Operational Excellence
• DevOps Toolchains, ITSM Platforms, and Enterprise System Integration
• Monitoring, Observability, Telemetry, and Performance Analytics
• Cloud Platforms, Infrastructure Automation, and Scalable Technology Solutions
• AI Platform Operations, Lifecycle Management, and Scalability Planning
• Security Standards, Risk Management, and Enterprise Compliance
• Stakeholder Management, Cross-Functional Collaboration, and Change Leadership
• Technical Leadership, Team Mentoring, and Engineering Best Practices
• Agile Methodologies, Continuous Delivery, and Software Engineering Processes
• Problem Solving, Strategic Thinking, and Decision-Making Skills
• Technical Documentation, Reference Architectures, and Knowledge Sharing
• AI Adoption Strategies, Organizational Enablement, and Transformation Initiatives
• Innovation Management, Emerging Technologies, and Continuous Improvement

Education

• Bachelor’s Degree in Computer Science, Software Engineering, Information Technology, Artificial Intelligence, or a related technical discipline.
• Master’s Degree in Artificial Intelligence, Computer Science, Information Systems, Engineering, or a related field is preferred.
• Certifications in Artificial Intelligence, Cloud Technologies, DevOps, Enterprise Architecture, Platform Engineering, or Automation Technologies are highly desirable.
• Equivalent practical experience in Software Engineering, Platform Engineering, AI Automation, Enterprise Architecture, or Technology Leadership will be strongly considered.