AI Agent Orchestration Lead

COOK SYSTEMS has an immediate need for an AI Agent Orchestration Lead The AI Agent Orchestration Lead is a senior, hands‑on engineering leader responsible for operationalizing AI agents end to end across the Software Development Life Cycle (SDLC). Working within the organization’s established AI framework, governance model, DevOps platform, and Enterprise Architecture standards, this role focuses on moving beyond pilots and experiments and into repeatable, production‑grade capability that teams live with daily. This role partners closely with Solution Architects, Enterprise Architects, delivery teams, security partners, platform teams, and IT leadership to coalesce fragmented efforts, align on standard patterns, and lay a durable foundation for scaled AI‑driven SDLC automation. The emphasis is on implementation, adoption, reliability, and outcomes—not one‑off demos. The AI Agent Orchestration Lead acts as a foundational builder, ensuring AI agents are not only introduced, but operated, governed, sustained, and continually improved as part of normal delivery. Key Responsibilities: AI Agent Implementation, Orchestration & Operationalization: Design, implement, and operationalize AI agents that automate and augment SDLC activities end to end (e.g., intake, planning, development support, testing, release validation, documentation, and operational support). Build production‑grade agent orchestration workflows using approved platforms, frameworks, and standards, with clear ownership, failure handling, and lifecycle management. Ensure agents are durably embedded into delivery workflows, not run as isolated experiments or sidecar tools. Leverage and extend approved agent templates, prompts, components, and orchestration patterns to drive consistency and scale. Implement and mature human‑in‑the‑loop controls, approvals, escalation paths, and audit mechanisms as first‑class operational features. SDLC Foundation & Toolchain Integration: Embed AI agents directly into the existing DevOps and delivery toolchain, including backlog management, CI/CD, testing frameworks, ITSM, observability, and knowledge platforms. Focus on end‑to‑end flow, ensuring agent outputs are actionable, traceable, and consumable in downstream SDLC stages. Align integrations with approved integration patterns, security models, identity controls, and operational standards. Avoid fragmentation by coalescing overlapping pilots and approaches into shared, supported implementations. Architectural Alignment & Governance‑by‑Design: Operate within Enterprise Architecture guardrails, ensuring agent ecosystems reinforce—not bypass—architectural intent. Embed AI governance, responsible AI practices, compliance controls, and auditability directly into agent design and orchestration. Partner with architecture, security, risk, and platform teams to review designs early and continuously, enabling speed with confidence. Ensure AI agents can be supported, monitored, and evolved like any other production platform capability. SDLC Optimization, Adoption & Continuous Improvement: Identify high‑value SDLC automation opportunities aligned to enterprise frameworks and operational maturity—not novelty. Drive real adoption across delivery teams, helping teams move from “trying” AI to relying on it. Deliver and communicate measurable improvements in cycle time, quality, stability, and developer experience. Use existing telemetry, observability, and reporting mechanisms to prove value and guide prioritization. Enablement, Alignment & Leadership: Act as a hands‑on technical partner and force multiplier for delivery teams adopting AI‑augmented workflows. Provide clear reference implementations, playbooks, and guidance that teams can operationalize independently. Help align stakeholders across architecture, security, platform, and delivery to reduce friction and accelerate adoption. Provide executive‑ready updates that focus on operational progress, adoption, risks, and outcomes—not experimentation metrics. Required Qualifications: 8+ years of experience in software engineering, DevOps, platform engineering, or solutions architecture, with a strong bias toward delivery and operations. Demonstrated experience operationalizing SDLC automation on enterprise platforms (not just piloting tools). Hands‑on experience implementing governed AI solutions in production environments. Proven ability to coalesce teams, align approaches, and drive standardization without heavy formal authority. Strong communication skills, with the ability to translate technical capability into delivery outcomes. Preferred Qualifications: Bachelor’s degree in Computer Science, Engineering, or equivalent experience. Experience designing or operating AI agents and agent orchestration frameworks at scale. Familiarity with mature AI governance, responsible AI practices, and audit requirements. Strong execution‑oriented engineering mindset with platform and operational thinking. Measures of Success: Operational adoption of AI agents across delivery teams using approved, repeatable patterns. AI agents functioning as a normal, trusted part of the SDLC, not isolated pilots. Demonstrated improvements in delivery speed, quality, and stability. Sustained compliance with architecture, security, and AI governance standards. e/o/e