As an AI Orchestrator, you do not execute a specific step in the pipeline, but rather conduct the entire flow. Your main responsibilities will include:
Mapping and Governance: Map the SDLC Value Stream, defining, reviewing, and evolving processes and rituals for a continuous model with AI.
Flow and Bottleneck Management: Govern end-to-end delivery, reducing lead time, monitoring workflow health, and proactively eliminating bottlenecks without compromising cost and quality.
Hybrid Orchestration: Orchestrate workflows between people and agents throughout the cycle, maintaining a continuous flow and handling exceptions.
Compliance and Security: Define levels of autonomy for agents and triggers for human sign-off, ensuring compliance with security, privacy, ethical, and regulatory requirements.
Demand Translation (Upstream/Tech): Bridge the gap with technical execution, translating demands to ensure feasibility, defining success metrics (ROI, hit rates), and prioritizing value generation.
Financial Sustainability: Assess the feasibility and ROI of agentic solutions, approving architectures that balance precision, response time, and token consumption.
Dependency Management: Manage technical dependencies between the AI Engineer and the AI Deployment Engineer, understanding AI capabilities and failure modes to organize the sequence of work.
Quality Assurance: Ensure compliance with validation checkpoints (architecture, compliance, QA), handling escalations when outputs diverge.
Change Management: Drive the evolution of ways of working, developing the squad’s technical fluency and engaging the team in the cultural transition to the new orchestration model.