Associate Director, Applied AI Engineering-PxE Workplace Experience

About the Role

As an Associate Director of 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.

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.

Responsibilities

• Strategic Vision and Alignment: Craft and articulate a vision for Applied AI engineering across the firm's enterprise solutions, aligning 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. 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.
• 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. Remain hands-on with design, architecture, and code while reviewing code, driving tech-debt reduction, and experimenting with new technology.
• Capability Evolution and Development: Mentor and develop engineers and emerging engineering leaders, coaching modern Applied AI engineering practices such as full-stack and microservices, 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, including speaking at conferences, publishing whitepapers or blogs, and leading R&D collaborations.
• Iterative Value Delivery: Embrace an iterative and incremental approach to Applied AI product engineering, favoring rapid learning and action. Apply empirical methods to navigate complexity and uncertainty, 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, driving teams toward peak performance through continuous learning and collaboration.
• Expert Proficiency and Continuous Improvement: Possess deep expertise in modern Applied AI engineering and architecture practices. Continuously enhance the engineering operating model to be lean, adaptable, and responsive, fostering a culture of innovation.
• Tech/Quality Risk Management: Establish and evolve reference architectures, coding standards, and engineering benchmarks to ensure robust, secure, scalable, and reliable solutions. Ensure responsible technology adoption, developing explainable, scalable, and secure AI and agentic products. Proactively identify and mitigate technical risks.
• 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 executives—building collaborative 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.
• 10+ 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, and unit/integration testing frameworks.
• 7+ 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) and cloud-native engineering using FaaS, PaaS, and microservices on Azure, AWS, or GCP, including their AI/ML services (e.g., Azure OpenAI, AWS Bedrock, Vertex AI). Experience with application-level infrastructure-as-code and 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.
• 2+ years of experience establishing engineering standards, actively leading, mentoring, and guiding team members 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.
• Candidates must be located within a commutable distance to one of the select locations available for this role and able to work in the local office at least 3 days per week.

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. 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.

Pay

A reasonable estimate of the current range is $130,900 to $268,700. You may also be eligible to participate in a discretionary annual incentive program, subject to program rules, where awards depend on individual and organizational performance.

Limited immigration sponsorship may be available.