Director, Applied AI Engineering

Role Overview: As a Director, Applied AI Engineering , you will shape and own the engineering strategy and technical direction for your service line-translating business objectives into engineering strategy, mapping business capabilities to the enterprise technology landscape, and defining how GenAI and agentic capabilities are built directly into the products we deliver. You will develop and execute a forward-looking technology roadmap that drives simplification, scalability, and efficiency-rationalizing the landscape and integrating the service line's portfolio within the wider enterprise across multiple upstream and downstream systems. Leading across product groups and the service line, you will stay hands-on in your craft-shaping architecture, integration, design, and code-while driving the standards and enterprise reference architectures that engineers build against.

Your leadership will be pivotal in delivering tangible value across the service line's product and AI investments, aligning technical solutions with business and technology strategy, and advancing Applied AI engineering across the organization. You will bring extensive engineering craftsmanship and deep expertise across software and data engineering, enterprise and integration architecture, and AI/ML and GenAI, together with an exemplary track record of high-quality, outcome-focused delivery at scale. The ideal candidate is a role-model engineering leader who leads by doing -owning strategy, elevating standards, developing engineers and emerging leaders, and building trusted relationships with stakeholders from engineering teams to senior executives and service-line leadership.

Key Responsibilities

Strategic Vision and Alignment: Accountable for defining, communicating, and continuously refining the engineering strategy for the service line-translating business objectives into actionable strategy, mapping business capabilities to the enterprise technology landscape, defining how GenAI and agentic capabilities are built directly into the products we deliver, and shaping how those products integrate within the enterprise across multiple upstream and downstream systems-in alignment with the Business Strategy and US Deloitte Technology strategy. Collaborate with diverse stakeholders and executives, including businesses and enabling areas as well as product, engineering, experience, delivery, security, and infrastructure teams, across all organizational levels.
Advocacy and Technology Roadmap: Champion, own, and execute the integrated Applied AI engineering, architecture, and technology strategy and its implementation roadmap across the service line-driving simplification, scalability, and efficiency, and actively rationalizing the landscape by removing unnecessary systems, integrations, and bottlenecks. 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, interoperability, auditability, and maintainability, and own engineering health and delivery KPIs across the product groups and the service line. Establish and evolve Applied AI engineering, enterprise and integration 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, integration, and code-contributing to product group and service line velocity and staying engaged with engineers across the SSDLC-while reviewing standards and 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 leaders, building the engineering talent bench across the product groups and the service line. Coach modern Applied AI engineering practices-full-stack and micro-services, integration tools and practices, 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 and communities. 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 and integration architecture, 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, functionality, and integration approaches that do not add value-and drive teams toward peak performance through continuous learning and collaborative execution. Collaborate, challenge, and own technical decisions advocated by business or other groups that do not fit or advance the enterprise ecosystem.
Expert Proficiency and Continuous Improvement: Possess deep expertise in modern Applied AI engineering and