Associate Director, Applied AI Engineering - PxE Consulting

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.

Responsibilities

• Craft and articulate a vision for Applied AI engineering across the firm's enterprise solutions, aligning with Business Strategy and US Deloitte Technology strategy.
• Collaborate with diverse stakeholders across product, engineering, experience, delivery, security, and infrastructure at all organizational levels.
• Advocate for, develop, and communicate the integrated Applied AI engineering, architecture, and technology strategy and its implementation roadmap to engineering teams and business stakeholders.
• Promote a culture of reuse, quality, and speed, focusing on leverage of existing assets and cost management (inference, token, and cloud costs).
• 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, contributing to team and product group velocity, reviewing code, driving tech-debt reduction, and experimenting with new technology.
• Mentor and develop engineers and emerging engineering leaders, coaching modern Applied AI engineering practices (full-stack, micro-services, cloud-native design, AI/ML/GenAI, data engineering, infrastructure-as-code, advanced deployment techniques).
• Lead by example through thought leadership, showcasing experiments internally, speaking at conferences, publishing whitepapers or blogs, and leading R&D collaborations.
• Embrace an iterative and incremental approach to Applied AI product engineering, favoring rapid learning and value delivery aligned with customer and business goals.
• Maintain a relentless focus on solving critical customer challenges, aligning technical solutions with business outcomes and minimizing unnecessary complexity.
• Continuously enhance the engineering operating model to be lean, adaptable, and responsive, fostering a culture of innovation.
• 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 technical risks and develop mitigation strategies.
• Influence and drive decision-making across the organization through clear, structured communication of technical trade-offs.
• Engage stakeholders at all levels, building collaborative relationships and co-creating momentum and value.

Requirements

• Bachelor's degree in computer science, software engineering, data science, machine learning, or 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, unit and integration testing frameworks.
• 7+ years of experience architecting and delivering enterprise solutions on modern technology stacks (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 micro-services on Azure, AWS, or GCP, including AI/ML services (Azure OpenAI, AWS Bedrock, Vertex AI) and application-level infrastructure-as-code with 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.
• Experience with Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, code instrumentations, and AI-augmented spec-driven development.
• Experience using methodologies & 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).
• Ability to work in your local office at a minimum of 3 days per week.
• Must be located within a commutable distance to one of the select locations available for this role.
• Must be legally authorized to work in the United States without the need for employer sponsorship.
• Ability to travel 0-10%, on average, based on the work you do and the clients and industries/sectors you serve.

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

The wage range for this role is $130,900 to $268,700. At Deloitte, it is not typical for an individual to be hired at or near the top of the range, and compensation decisions depend on various factors, including skill sets, experience, training, and organizational needs. 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.