Strategic Vision and Alignment: Craft and articulate a vision for Applied AI engineering across the firm's enterprise solutions-mapping business capabilities to the enterprise technology landscape and defining how GenAI and agentic capabilities are built directly into the products we deliver-in alignment with the Business Strategy and US Deloitte Technology strategy. Collaborate with diverse stakeholders across product, engineering, experience, delivery, security, and infrastructure at all organizational levels.

Advocacy and Technology Roadmap: Advocate for, develop, and communicate the integrated Applied AI engineering, architecture, and technology strategy and its implementation roadmap to engineering teams and business stakeholders. Ensure the organization is well-informed about objectives, KPIs, maturity, compliance, and progress. Promote a culture of reuse, quality, and speed-keeping an eye on leverage of existing assets and on the inference, token, and cloud cost of what we build, to maximize outcomes and minimize total cost.

Craft Mastery and Objectives Realization: Define, measure, and drive the achievement of KPIs and NFRs spanning system performance, scalability, security, reliability, and maintainability. Establish and evolve Applied AI engineering, architecture, and AI/ML/GenAI reference architectures, standards, and best practices-including spec- and context-driven development, evaluations, AI agent orchestration, and the AI and Agentic SSDLC that carries work from discovery to production to operations with full automation and quality checks through the SSDLC lifecycle. Remain hands-on with design, architecture, and code-contributing to team and product group velocity and staying engaged with engineers across the SSDLC-while reviewing code, driving tech-debt reduction, and experimenting with new technology.

Capability Evolution and Development: As a recognized engineering leader, mentor and develop engineers and emerging engineering leaders, coaching modern Applied AI engineering practices-full-stack and micro-services, cloud-native design, AI/ML/GenAI and agentic systems, data engineering, application-level infrastructure-as-code, and advanced deployment techniques (Blue-Green, Canary, A/B testing) that minimize downtime. Lead by example through thought leadership-showcasing experiments internally, speaking at conferences, publishing whitepapers or blogs, and leading R&D collaborations, including with academia. Cultivate a growth mindset and modern engineering behaviors across the organization.

Iterative Value Delivery: Embrace an iterative and incremental approach to Applied AI product engineering, favoring action and rapid learning over extensive upfront planning. Apply a leaning-forward approach and empirical methods to navigate complexity and uncertainty, ensuring each iteration delivers value and stays aligned with customer and business goals.

Customer-Centric Problem Solving: Maintain a relentless focus on solving the most critical challenges faced by customers and users, aligning technical solutions with business outcomes. Minimize unnecessary technical complexity and avoid overengineering-features and functionality that do not add value-and drive teams toward peak performance through continuous learning and collaborative execution.

Expert Proficiency and Continuous Improvement: Possess deep expertise in modern Applied AI engineering and architecture practices, with a keen ability to identify inefficiencies and opportunities for innovation across the product lifecycle. Continuously enhance the engineering operating model to be lean, adaptable, and responsive-guiding and transforming the organization to embrace lean principles and foster a culture of innovation.

Tech/Quality Risk Management: Establish and evolve reference architectures, coding standards, and engineering and quality benchmarks that ensure robust, secure, scalable, and reliable/resilient solutions. Ensure appropriate, responsible technology adoption-developing explainable, scalable, reliable, and secure AI and agentic products-and proactively identify technical risks, developing mitigation strategies through proactive problem-solving and contingency planning.

Influential Communication: Influence, persuade, and drive decision-making across the organization. Communicate effectively in both written and verbal forms, crafting clear, structured arguments and technical trade-offs supported by evidence.

Organizational Engagement and Collaboration: Engage stakeholders at all levels-from team members to middle management to executives-building collaborative, constructive relationships and co-creating momentum and value across multiple organizational levels.

Associate Director, Applied AI Engineering

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