Senior Director, AI Operations (AI/LLM Production Systems)

AECOM•Dallas, TX

1d•Hybrid

About The Position

We’re defining how AI runs in production across the enterprise. As AI adoption scales, the challenge shifts from building models to operating them reliably. This role owns how AI/LLM and agentic systems are run, supported, and governed, ensuring they are reliable, observable, cost-efficient, and continuously improving in real-world environments. You will lead the development of the enterprise AI Operations practice, establishing the standards, operating model, and visibility required to support AI at scale. This includes defining how systems are monitored, how incidents are managed, how risks are controlled, and how performance is continuously improved. Working closely with Engineering, AI Platform, Product, and Delivery teams, you will ensure all production AI systems meet clear operational standards and that leadership has consistent visibility into system health, performance, and risk. This is a hands-on, senior leadership role with end-to-end accountability for how AI systems perform in production.

Requirements

Bachelor's Degree plus extensive years of SRE, MLOps, production operations, or platform engineering experience, including 6 years of leadership experience, or demonstrated equivalency of experience and/or education
Experience operating AI/ML/LLM systems in production (serving real users at scale) with clear ownership and accountability
Background in SRE, MLOps, or distributed systems, with depth in reliability and operational excellence
Strong understanding of AI production failure modes (e.g., drift, hallucinations, orchestration issues, cost inefficiencies)
Experience building and scaling observability, monitoring, and telemetry systems (e.g., OpenTelemetry, Datadog, Prometheus, Grafana)
Proven track record defining SLAs/SLOs, incident management, and operational frameworks for complex systems
Experience leading cross-functional efforts across engineering, platform, and product teams
Ability to operate at both strategic and hands-on levels, setting direction while driving execution

Nice To Haves

Experience with LLM platforms or frameworks (e.g., Azure AI, AWS Bedrock, LangChain)
Experience with agentic systems, RAG pipelines, or orchestration frameworks
Background in ITIL or service management, applied to modern distributed systems
Familiarity with Responsible AI and governance frameworks

Responsibilities

Define and scale the enterprise AI Operations practice, including operating model, standards, and governance
Establish production readiness and operability standards across AI/LLM and agentic systems
Own production reliability, including SLAs/SLOs, incident management, and support models
Implement observability and monitoring for AI systems (latency, drift, behavior, failures, cost)
Ensure clear ownership, escalation paths, and accountability across production AI systems
Build controls for agent behavior, model usage, and operational risk
Drive performance, reliability, and cost optimization across AI workloads
Lead operational reviews and reporting, providing visibility into system health, risks, and trends
Identify systemic issues and drive continuous improvement across AI systems and processes
Partner with Engineering, Product, and Platform teams to ensure production readiness and alignment