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.

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 integrate within the enterprise across multiple upstream and downstream systems. Collaborate with diverse stakeholders and executives, including business, 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—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 inference, token, and cloud costs to maximize outcomes and minimize total cost.
• Craft Mastery and Objectives Realization: Define, measure, and drive 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, infrastructure-as-code, 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.
• Iterative Value Delivery: Embrace an iterative and incremental approach to Applied AI product engineering and integration architecture, favoring rapid learning and action over extensive upfront planning. Apply empirical methods to navigate complexity, ensuring each iteration delivers value aligned with customer and business goals.
• Customer-Centric Problem Solving: Maintain a relentless focus on solving critical customer challenges, aligning technical solutions with business outcomes. Minimize unnecessary technical complexity and avoid overengineering. Drive teams toward peak performance through continuous learning and collaborative execution. Challenge and own technical decisions that do not advance the enterprise ecosystem.
• Expert Proficiency and Continuous Improvement: Possess deep expertise in modern Applied AI engineering and architecture practices. Identify inefficiencies and opportunities for innovation across the technology landscape and product lifecycle. Continuously enhance the engineering operating model to be lean, adaptable, and responsive, fostering 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 solutions. Ensure responsible technology adoption, developing explainable, scalable, and secure AI and agentic products. Proactively identify technical risks and develop mitigation strategies to ensure operational excellence and resilience.
• Influential Communication: Influence and drive decision-making across the organization. Communicate effectively in 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—building collaborative relationships and co-creating momentum and value across the organization.

About the Team

US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost-effective model focused 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. It powers Deloitte's success and serves many of the world's largest, most respected companies. The team develops and deploys cutting-edge internal and go-to-market solutions that help Deloitte operate effectively and lead in the market.

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 establishing enterprise engineering standards, including actively leading, mentoring, and guiding large engineering teams in adoption and continuous improvement.
• 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).
• Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities with passion, empathy, and care.

Requirements

• 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.
• 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, where an award, if any, depends on various factors, including individual and organizational performance.