Director, AI Operations & Optimization

Vizient•Chicago, IL

3d•$117,600 - $206,000•Onsite

About The Position

In this role, the Director, AI Operations and Optimization will lead the operationalization, reliability, optimization, and continuous improvement of enterprise AI capabilities across Vizient. This leader is responsible for establishing scalable AI runtime operational practices, advancing AIOps and LLMOps capabilities, implementing observability and monitoring frameworks, and driving operational excellence for production AI solutions. The Director will oversee the operational support and continuous improvement of AI-powered applications, agentic workflows, and reusable AI platform capabilities while ensuring reliability, governance, security, and performance at enterprise scale. Through cross-functional collaboration and strong operational leadership, this role will help enable Vizient’s enterprise AI transformation strategy by delivering sustainable, scalable, and responsible AI operations.

Requirements

Bachelor’s degree in Computer Science, Information Systems, Engineering, Technology Management, or a related field preferred.
8+ years of experience in AI operations, software engineering, platform operations, engineering delivery, DevOps, Site Reliability Engineering (SRE), infrastructure operations, or related enterprise technology functions required.
3+ years of experience leading operational teams, engineering support organizations, platform operations, or large-scale technology initiatives required.
Hands-on experience supporting, operationalizing, monitoring, or optimizing production AI solutions utilizing large language models (LLMs), APIs, agentic workflows, orchestration frameworks, and modern AI engineering practices required.
Strong experience implementing and scaling operational support models, observability practices, incident management processes, DevOps methodologies, runtime operations, or enterprise operational frameworks required.
Experience with observability platforms, monitoring tools, incident management processes, runtime operations, CI/CD pipelines, and production support practices required.
Experience leading distributed teams, managing contractors and vendors, and delivering operational initiatives within complex and evolving environments required.
Experience with cloud platforms, APIs, data integration technologies, automation frameworks, monitoring solutions, DevOps tools, and modern operational toolsets required.
Strong analytical, problem-solving, communication, presentation, stakeholder management, and cross-functional collaboration skills required.
Demonstrated ability to manage multiple priorities in fast-paced, evolving, and operationally dynamic environments required.

Nice To Haves

Experience supporting enterprise-scale AI, automation, digital transformation, or platform modernization initiatives preferred.
Knowledge of AI governance, responsible AI principles, operational risk management, and production AI lifecycle management preferred.

Responsibilities

Lead enterprise AI operational activities, including runtime monitoring, operational support, incident management, production reliability, and operational continuity for AI-powered applications and intelligent automation solutions.
Establish, implement, and continuously improve AI operational practices, including AIOps and LLMOps processes, runtime observability, operational telemetry, drift detection, release coordination, support workflows, and operational readiness activities.
Drive runtime stability and service reliability initiatives through production monitoring, escalation management, root cause analysis, operational playbooks, and service continuity practices.
Support enforcement of runtime governance standards, operational safeguards, human oversight controls, and secure operationalization practices for enterprise AI solutions.
Ensure operational excellence across AI environments through proactive monitoring, issue prevention, and continuous service improvement efforts.
Lead initiatives focused on runtime efficiency, operational scalability, inference utilization, supportability, performance optimization, and sustainable AI operations.
Support the implementation and optimization of reusable operational patterns, observability frameworks, support standards, telemetry pipelines, operational tooling, and AI support capabilities.
Promote standardized operational processes, scalable support models, automation opportunities, and continuous improvement initiatives across AI operations functions.
Drive operational maturity by identifying opportunities to enhance performance, reduce operational risk, and improve support effectiveness.
Partner closely with AI Engineering & Delivery, AI Governance, AI Quality Engineering, Automation, Architecture, Platform Engineering, Security, Infrastructure, and business stakeholders to ensure operational readiness and runtime reliability.
Coordinate operational execution activities across AI operations teams, including operational planning, vendor and contractor management, issue prioritization, escalation management, knowledge transfer, and delivery continuity.
Support operational assessments, production readiness reviews, implementation planning, runtime support strategies, and modernization initiatives for prioritized AI capabilities.
Collaborate with technical and business leaders to align operational practices with enterprise AI objectives and service expectations.
Lead, mentor, and develop operations managers, engineers, analysts, and contractor resources while fostering a high-performing, collaborative, and continuously learning culture.
Provide clear communication regarding operational performance, runtime risks, service reliability concerns, optimization opportunities, engineering tradeoffs, and strategic recommendations.
Establish accountability for operational outcomes while promoting operational discipline, innovation, and continuous improvement.
Research and evaluate emerging AI operational technologies, observability platforms, automation capabilities, optimization techniques, and runtime management practices to drive innovation and operational effectiveness.