We seek an Executive Director to architect and operationalize the AI strategy for Image as a Service in Buenos Aires (Hybrid), reporting to the Head of Image as a Service. This individual contributor will exert significant indirect leadership to uplift AI adoption, establish governance, and standardize tooling across a global engineering function. The mandate includes delivering a greenfield digital twin for infrastructure, embedding AI to reduce toil and improve reliability, and partnering across the firm to modernize image lifecycle management. The ideal leader combines deep applied AI/ML expertise with engineering acumen and the ability to drive change in a complex environment.
Job Title & Details
- Title: Executive Director, Applied AI/ML – Image as a Service
- Location: Buenos Aires, Argentina (Hybrid)
- Reports to: Head of Image as a Service
- Role Type: **Individual contributor **
About the Team
Image as a Service engineers and supports Linux and Windows operating system images for the firm, providing the foundational platforms on which critical applications run. The team’s remit spans image creation, maintenance, security posture, and lifecycle management to ensure consistent, reliable, and compliant environments at enterprise scale.
Role Summary
This role will define and deliver the AI/ML strategy for Image as a Service to materially improve engineering efficiency, standardize solutions, and institutionalize responsible AI practices. The Executive Director will drive firmwide adoption of AI within the image engineering and SRE domains, establish a durable use case governance framework, and lead the creation of a greenfield infrastructure digital twin to enable simulation, validation, and automation at scale. Success is measured by demonstrable reductions in operational toil, accelerated delivery, stronger risk controls, and a cohesive AI tooling ecosystem that becomes integral to the team’s operating model.
Job responsibilities
- Develop and execute a comprehensive AI/ML strategy aligned to firm objectives and the Image as a Service roadmap, with clear outcomes for efficiency, reliability, and risk management.
- Drive adoption of AI across internal teams to reduce toil, optimize workflows, and embed AI into day-to-day engineering practices and decisioning.
- Lead the ideation, design, and development of a greenfield infrastructure digital twin, progressing from early proofs of concept to a scalable platform for simulation, validation, and change impact analysis.
- Establish and run a use case governance framework covering intake, approval, model risk controls, responsible AI practices, and lifecycle oversight to ensure safety, compliance, and value realization.
- Standardize AI tooling and solutions to avoid fragmentation, selecting and scaling one strong solution where appropriate, and deprecating duplicative approaches.
- Recommend and implement targeted skills uplift programs, including hands-on enablement and tooling familiarity (e.g., GitHub Copilot), to accelerate practitioner productivity and code quality.
- Provide indirect leadership, mentoring, and architectural guidance on AI/ML best practices for engineers working across Linux and Windows image pipelines.
- Evaluate emerging AI/ML technologies and patterns for applicability to OS image engineering, testing, configuration management, patch orchestration, telemetry, and incident reduction.
- Partner with stakeholders across infrastructure, security, compliance, and application teams to identify and prioritize high-impact AI use cases in image lifecycle management and support.
Required qualifications, capabilities, and skills
Deep applied AI/ML expertise, including hands-on development, deployment, and operationalization of machine learning and AI-driven solutions in production.
Proven engineering leadership with the ability to influence and direct cross-functional teams without direct reporting lines, and to drive change at enterprise scale.
Strong understanding of infrastructure technologies and the engineering of Linux and Windows operating system images, or closely related domains.
Demonstrated success establishing governance frameworks, standardizing tooling, and institutionalizing responsible AI practices in complex organizations.
Exceptional communication and stakeholder management skills, capable of translating advanced AI concepts for both technical and non-technical audiences.
No minimum years of experience required; emphasis on depth of expertise, impact, and delivery.
Preferred qualifications, capabilities, and skills
Experience designing or operating digital twin solutions for infrastructure, including data modeling, simulation fidelity, and integration with engineering workflows.
Track record of embedding AI into platform engineering practices to improve reliability, performance, and security outcomes.
Familiarity with developer productivity tooling and enablement approaches, including generative AI-assisted development and code quality acceleration.
Exposure to model risk management, monitoring, and continuous validation approaches suited to infrastructure-focused AI/ML solutions.