We are seeking a AI Tech Transformation Manager responsible for ensuring the seamless operation, maintenance, and optimization of enterprise Data & AI systems.
This role is pivotal in transforming Analytics, Machine Learning, and Generative AI initiatives from development into reliable, scalable, and high-performing operational environments. The position requires a technically grounded leader with strong expertise in Data & AI architecture and operational frameworks, capable of managing complex, multi-regional teams and driving a culture of operational excellence and continuous improvement.
Key Responsibilities
- Lead the operation, maintenance, and continuous enhancement of all Data & AI production systems, including Generative AI models and agentic platforms, ensuring stability, reliability, and scalability.
- Define, implement, and monitor KPIs and SLAs for Data & AI operations, proactively identifying and addressing deviations.
- Establish and manage data quality frameworks to guarantee accuracy, consistency, and integrity across AI and data environments.
- Oversee incident management and problem resolution processes, ensuring thorough root cause analysis and prevention strategies.
- Collaborate with Data Science and AI Engineering teams to embed operational efficiency, observability, and maintainability into solution design.
- Optimize model and pipeline performance in production, identifying bottlenecks and applying improvements to enhance speed and efficiency.
- Build and maintain strong partnerships with business and technical stakeholders, providing transparent communication on system health and operational changes.
- Lead and mentor teams of Data & AI operations professionals, promoting best practices, continuous learning, and collaboration.
- Develop and standardize operational documentation, runbooks, and training to ensure consistency across Data & AI operations.
- Evaluate and integrate emerging tools and technologies to enhance automation, monitoring, and operational effectiveness.
- Ensure alignment with responsible AI and data governance policies throughout the system lifecycle.
- Initially oversee Data & Analytics Solution Architecture, with potential evolution of scope over time.
Qualifications
- Bachelor’s degree in Computer Science, Engineering, Business Analytics, or a related discipline (advanced degree preferred).
- At least 10 years of experience building and operating Data & AI solutions, including 5+ years in leadership roles focused on MLOps, DataOps, or GenAIOps.
- Proven success in managing complex, large-scale Data & AI systems with high performance and availability.
- Strong background in data quality management, monitoring, and remediation strategies for AI/ML and data platforms.
- Excellent communication and relationship-building skills, with the ability to influence senior technical and business stakeholders.
- Demonstrated experience leading technical teams and fostering a culture of operational excellence and improvement.
- Hands-on experience in production support, incident management, and problem-solving for Data & AI applications.
- In-depth understanding of cloud ecosystems (preferably GCP) and related services for Data & AI operations.
- Knowledge of monitoring, logging, and automation frameworks relevant to production environments.
- Experience managing vendors, contracts, and budgets.
- Ability to perform effectively in dynamic, fast-changing environments, balancing short-term execution with long-term scalability.
- Willingness to travel across regions within the Americas several times per year.
- Preferred: experience in multinational, cross-functional organizations within CPG, retail, or high-tech sectors.
Technical & Change Expertise
- Deep expertise in MLOps, DataOps, and GenAIOps frameworks.
- Advanced knowledge of cloud-based Data & AI ecosystems and architectures (GCP preferred).
- Strong command of monitoring, automation, and reliability engineering practices for production AI systems.
- Familiarity with agentic frameworks and generative AI operationalization.
- Proven ability to establish and govern data quality and compliance frameworks across AI initiatives.
Leadership & Communication
- Skilled at leading distributed technical teams across multiple regions and time zones.
- Effective communicator, capable of translating complex technical insights into actionable, business-relevant messages.
- Strong stakeholder management, fostering trust and collaboration across technical and non-technical audiences.
- Leadership approach centered on empowerment, accountability, and continuous improvement.