Overview
Summary / Goal
Autonomously deliver ongoing business impact across a business data domain through owning and achieving semi-annual to annual team goals. Demonstrate expertise in identifying effective solutions for ambiguous, open-ended problems requiring strategic prioritization, defining technical solutions and operational processes that elevate team performance. Serve as a strong leader with growing influence beyond the team by driving cross-team and cross-discipline initiatives to optimize broader systems. Play a key role in setting medium-to-long-term technical strategy for business-critical projects and independently defining and delivering technical roadmaps for large, cross-team efforts, maintaining deep product expertise by integrating customer needs and stakeholder input to deliver customer value rapidly, and mentoring less-experienced team members through guidance and leading by example.
Key Responsibilities
Strategic Business Advisory through domain knowledge and functional acumen
Lead the design, development, and deployment of complex, scalable AI/ML solutions across multiple projects within a business data domain. Act as interlink between the Business Owners within a specific business data domain, and the AI solutioning
Apply and demonstrate deep mastery of advanced AI/ML frameworks. Own and continuously refine the MLOps strategy by implementing sophisticated solutions for continuous integration and delivery, automated model monitoring, and resilient production infrastructure. Mentor AI engineers on model optimization, system reliability, and advanced debugging techniques to elevate team capability and engineering excellence.
AI/ML Architecture Design and System Design
Decompose complex problems and business scenarios into cohesive solutions comprising multiple interacting software and data model components. Proactively identify and surface technical and data dependency issues across cross-team boundaries, enabling timely resolution and alignment to ensure successful project delivery. Independently design software and data components within well-scoped scenarios, prioritizing simplicity, maintainability, and testability. Ensure components are easily debuggable and expose clear, logical interfaces that minimize misuse, exercising sound judgment on when to refactor versus preserve existing implementations. Maintain deep expertise in relevant libraries, platforms, and systems, applying performance optimizations appropriately to meet data use case requirement
Stakeholder & Partner management
Advise and collaborate with senior stakeholders on best practices for AI/ML model deployment and operational standards, overseeing their impact on system performance and reliability, while building and maintaining a network of internal and external partners—including product owners, data scientists, and engineering teams—to inform, influence, and guide them on AI model development, monitoring, and analytics strategies.
Spearheading innovation
Stay pro-actively on top of evolutions in the field of AI: Continuously update and maintain advanced expertise to enhance AI/ML capabilities and ensure effective operation within the AI engineering team and broader organization, acting as a technical specialist by undertaking complex tasks to diagnose, analyze, and resolve challenging problems, maintaining expert-level knowledge of cutting-edge AI frameworks, model deployment practices, and industry trends, while actively sharing insights and contributing to knowledge transfer across teams and the wider technical community. Lead the evaluation and adoption of cutting-edge AI research into production systems, prototyping novel solutions
Responsible AI
Provide leadership and guidance, mentorship, and support to AI Engineers, fostering the adoption of best practices, coding standards, and problem-solving methodologies to build robust, secure, scalable, and AI-enabled data solutions.
Proactively identifying and leading efforts to address technical debt within AI/ML systems and infrastructure, ensuring long-term maintainability and cost-efficiency.
People Management (incl Coaching and Mentorship)
Recruit, lead, appraise and coach employees within the own functional area, to ensure the structure of the team is effective to achieve results and employees are engaged, motivated and supported to realize the objectives of the department/functional area. Acts an example for others and as a leader who ensures that individuals and groups buy into the strategy, plans, goals, climate and policy.
Provide senior-level technical leadership, guidance, and mentorship to AI engineers based on existing AI Governance Frameworks, by implementing and influencing technical direction and fostering a culture of automation, reliability, and shared ownership in AI/ML model development, deployment, and operations.