AI Orchestrator
Core
Orchestrate end-to-end delivery flow across the full Software Development Lifecycle (SDLC), connecting upstream demand with technical execution and final delivery by coordinating people, AI agents, workflows, and dependencies.
Role type
Delivery Orchestrator (AI-enabled workflows)
Builds
Executable solutions for end users via coordinated AI and human workflows
Domain
Software Development Lifecycle (SDLC) and AI-driven operations
Deliverable
production ML models | product features | dashboards & analysis | research | client delivery | infrastructure | physical/clinical work
Required skills
End-to-end delivery orchestration, SDLC and Value Stream governance, Systems thinking, Process and governance model design, Analytical decision-making, Success metrics definition (ROI, accuracy), Stakeholder management, AI fluency, AI workflow governance, Adaptability and continuous improvement
Preferred skills
Program/project management, Product delivery, Operations, Transformation, Facilitation, Coordination, Organizational skills
Technologies
AI agents, AI-driven workflows
Responsibilities
Orchestrate end-to-end delivery mapping and governing the value stream across the complete SDLC; Coordinate workflows involving people and AI agents across the delivery pipeline; Govern delivery performance to maintain appropriate levels of time, cost, quality, and compliance; Define, review, and continuously evolve delivery processes, standards, governance practices, and team rituals; Monitor and maintain workflow health, identifying bottlenecks, dependencies, exceptions, risks, and opportunities for improvement; Act as the bridge between upstream business or product demand and the technical execution performed by AI Engineers and AI Deployment Engineers; Translate requirements into actionable workflows while assessing the technical and financial feasibility of proposed solutions; Define and monitor success metrics such as ROI, accuracy rates, delivery performance, and other relevant value indicators; Prioritize initiatives and delivery activities based on the value they generate for the organization and end users; Govern AI-enabled workflows by managing dependencies, exception paths, levels of agent autonomy, and appropriate boundaries for human approval; Promote consistency across the delivery model while identifying opportunities to improve efficiency, quality, and scalability; Develop AI fluency across the team through continuous learning, enablement, and practical adoption of AI-driven ways of working; Support the squad in transitioning toward a model where teams effectively orchestrate AI agents and workflows rather than relying primarily on accumulating specialized capabilities; Facilitate continuous improvement of the overall delivery operating model and encourage experimentation with new AI-enabled practices
