Associate Director, Applied AI Engineering-PxE Talent

Associate Director, Applied AI Engineering

Role Overview: As an Associate Director, Applied AI Engineering , you will set the engineering vision and technical direction for 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. Leading across teams and product groups, you will stay hands-on in your craft-shaping architecture, design, and code-while driving the standards and reference architectures that engineers build against. Your leadership will be pivotal in delivering tangible value across Deloitte’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, solution 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 -setting vision, elevating standards, developing engineers and emerging leaders, and building trusted relationships with stakeholders from engineering teams to executives.

Key Responsibilities:

• 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…