About the Role -
The Manager of Data and AI Engineering leads and mentors a high-performing team responsible for designing, developing, deploying, and operationalizing enterprise-grade data and artificial intelligence solutions. This role bridges business priorities with technical execution, translating strategic objectives into scalable engineering roadmaps for data pipelines, MLOps frameworks, and production-ready AI systems. The Manager is accountable for the delivery of reliable, governed, secure and maintainable solutions that enable intelligent automation, predictive insight, and advanced analytics across the organization. By fostering engineering excellence, collaborating closely with data science, product, and business leaders, and growing technical talent, this position plays a critical role in scaling the organization's ability to leverage data and AI effectively while ensuring alignment with enterprise architecture and responsible AI standards.
Functions, Knowledge, and Skills
Leadership & Team Development
- People Leadership: Proven experience managing, mentoring, and developing high-performing technical teams, with a strong ability to guide Data Engineers, Machine Learning Engineers, and AI Engineers through complex challenges.
- Talent Management: Demonstrated ownership of the full talent lifecycle, including attracting, hiring, and onboarding top technical talent, as well as managing performance and fostering career development.
- Proactive Ownership: A self-starter mentality with a proactive approach to identifying and solving problems, driving initiatives forward, and inspiring a culture of excellence and accountability within the team.
Technical Strategy & Expertise
- Strategic Vision & Road mapping: Ability to think strategically and operate effectively within ambiguous environments, translating complex business requirements into clear technical roadmaps and end-to-end architectural designs.
- Technical Depth & Architectural Design: Strong technical background and decision-making authority across the full AI stack, with hands-on proficiency in:
- Data Engineering & Platforms: ETL/ELT, data warehousing, and big data technologies (e.g., Spark).
- ML System Design: Architecting scalable and maintainable machine learning systems.
- MLOps Practices: CI/CD, containerization (Docker, Kubernetes), automated model monitoring, feature stores, and lifecycle governance.
- Cloud Platforms: Deep knowledge of modern data stacks and cloud services (GCP, AWS, Azure), particularly their AI/ML offerings (e.g., Vertex AI, SageMaker, Azure ML).
- Generative AI Expertise: Deep conceptual and practical understanding of how generative AI systems work, with the ability to guide teams in designing efficient prompts and interactions to optimize model performance, accuracy, and cost.
- Economic & Pragmatic Judgment: Strong command of AI cost dynamics (e.g., tokenization, request patterns) to implement effective cost-optimization strategies. Critically evaluates when AI is not the right solution and directs teams toward simpler, more efficient alternatives.
Responsible AI & Enterprise Governance
- Experience implementing enterprise standards for responsible AI, including model governance, fairness, explainability, and security.
- Responsible for preventing redundant or fragmented AI solutions by driving standardization and ensuring new systems integrate seamlessly with existing enterprise APIs and data ecosystems.
- Risk Management for Automated Systems: Understanding of the risks associated with agent-based systems (e.g., cascading failures, uncontrolled API interactions) and the ability to design and enforce robust safeguards such as rate limiting, bounded execution, and controlled data access.
Execution & Collaboration
- Effective Communication: Exceptional communication and stakeholder management skills, with a proven ability to articulate complex technical concepts,
- risks, and outcomes to both technical and non-technical audiences, from individual contributors to senior leadership.
- Cross-Functional Collaboration: A natural ability to collaborate effectively across the organization, navigate complex stakeholder relationships, build consensus, and foster alignment even in challenging situations.
- Disciplined Execution: Promotes disciplined engineering practices over rapid experimentation when transitioning solutions to production, ensuring all AI solutions are evaluated for scalability, maintainability, and seamless integration within the broader enterprise ecosystem.
- Ecosystem Integration: Ensures that AI solutions are designed to integrate with existing enterprise systems, APIs, and data ecosystems, avoiding the creation of isolated or siloed implementations.
- Agile Project Management: Excellent understanding of Agile/Scrum methodologies for managing technical projects, engineering backlogs, and delivering results
Minimum Education
Bachelor's Degree in Information Systems, Computer Science, or a