Director, Applied AI Engineering

About the Role

As a Director of 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.

Responsibilities

• Strategic Vision and Alignment: Define, communicate, and continuously refine the engineering strategy for the service line—translating business objectives into actionable strategy, mapping business capabilities to the enterprise technology landscape, and shaping how GenAI and agentic capabilities are integrated into products. Ensure alignment with the Business Strategy and US Deloitte Technology strategy. Collaborate with diverse stakeholders, including business leaders, product teams, engineering, 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—driving simplification, scalability, and efficiency while 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, optimizing for inference, token, and cloud costs to maximize outcomes and minimize total cost.
• Craft Mastery and Objectives Realization: Define, measure, and drive the achievement of KPIs and non-functional requirements (NFRs) spanning system performance, scalability, security, reliability, interoperability, auditability, and maintainability. Own engineering health and delivery KPIs across product groups. 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. Remain hands-on with design, architecture, integration, and code, contributing to product velocity while reviewing standards, driving tech-debt reduction, and experimenting with new technology.
• Capability Evolution and Development: Mentor and develop engineers and emerging leaders, building the engineering talent bench across product groups. Coach modern Applied AI engineering practices, including full-stack and microservices, integration tools, cloud-native design, AI/ML/GenAI and agentic systems, data engineering, and advanced deployment techniques (Blue-Green, Canary, A/B testing). Lead by example through thought leadership—showcasing experiments, speaking at conferences, publishing whitepapers or blogs, and leading R&D collaborations with academia and communities. Cultivate a growth mindset and modern engineering behaviors.
• 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—driving teams toward peak performance through continuous learning and collaborative execution. 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 architecture practices, with a keen ability to identify inefficiencies and opportunities for innovation. Continuously enhance the engineering operating model to be lean, adaptable, and responsive—guiding the organization to embrace lean principles and foster a culture of innovation.
• Tech/Quality Risk Management: Establish and evolve enterprise reference architectures, coding standards, and engineering and quality benchmarks to ensure robust, secure, scalable, and reliable/resilient solutions. Ensure responsible technology adoption—developing explainable, scalable, secure AI and agentic products. Proactively identify technical risks and develop 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 senior executives and service-line leadership—building collaborative, constructive relationships and co-creating momentum and value across the organization.

Qualifications

• A bachelor's degree in computer science, software engineering, data science, machine learning, or a related discipline. Experience is the most relevant factor.
• 12+ years of full-stack software engineering experience with most of the following: Angular, React, NodeJS, Python, C#, .NET, Java, SQL/NoSQL, REST/SOAP/GraphQL, SSO/MFA, PyTorch, TensorFlow, LangChain, LangGraph, as well as unit and integration testing frameworks.
• 8+ years of experience architecting and delivering enterprise solutions on modern technology stacks (e.g., API Gateways, Message Brokers, Queuing Services, Workflow Automation & Orchestration, ETL/ELT, Event Streaming, Real-Time Data Processing, Service Mesh) and cloud-native engineering, using FaaS, PaaS, and microservices on cloud hyperscalers such as Azure, AWS, or GCP, including leveraging their AI/ML services (e.g., Azure OpenAI, AWS Bedrock, Vertex AI) and application-level infrastructure-as-code with cost-aware engineering (FinOps accountability).
• 5+ years of experience building AI/ML and agentic applications, with hands-on GenAI experience across LLM integration (OpenAI, Anthropic, or open-source models), RAG pipelines, prompt engineering, vector databases, evaluations, and AI agent orchestration.
• 5+ years of experience in establishing enterprise engineering standards, including actively leading, mentoring, and guiding large engineering teams in the adoption and continuous improvement of these standards.
• Prior software engineering experience with Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, code instrumentation, and AI-augmented spec-driven development.
• Prior experience using methodologies and tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (e.g., LangFuse, LangSmith, or equivalent multi-agent orchestration tools) to deliver high-quality products rapidly.
• Candidates must be located within a commutable distance to one of the select locations available for this role.
• Ability to work in your local office at a minimum of 3 days per week.

Additional Information

• Ability to travel 10%, on average, based on the work you do and products you build.
• Limited immigration sponsorship may be available.

Pay

A reasonable estimate of the current range is $151,400 to $311,000. You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

The Team

US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost-effective model that focuses on value and outcomes leveraging a progressive and responsive talent structure. As Deloitte's primary internal development team, Product Engineering delivers innovative digital solutions to businesses, service lines, and internal operations with proven bottom-line results and outcomes. It helps power Deloitte's success and serves many of the world's largest, most respected companies. We develop and deploy cutting-edge internal and go-to-market solutions that help Deloitte operate effectively and lead in the market. Our reputation is built on a tradition of delivering with excellence.