Associate Director, Applied AI Engineering

Deloitte•Jersey City, NJ

72d•$130,900 - $268,700•Hybrid

About The Position

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.

Requirements

A 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, as well as unit and 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, Service Mesh) and cloud-native engineering, using FaaS, PaaS, and micro-services on any of the cloud hyperscalers such as Azure, AWS, or GCP, including leveraging their AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI, plus 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 in establishing engineering standards, including actively leading, mentoring, and guiding team members in the adoption and continuous improvement of these standards.
Prior software engineering experience with the understanding of Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and code instrumentations, and AI-augmented spec-driven development.
Prior 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) etc. to deliver high-quality products rapidly.
Ability to work in your local office at a minimum of 3 days per week.
Candidates must be located within a commutable distance to one of the select locations available for this role.

Nice To Haves

Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.

Responsibilities

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.
Advocate for, develop, and communicate the integrated Applied AI engineering, architecture, and technology strategy and its implementation roadmap to engineering teams and business stakeholders.
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.
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.
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.
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—features and functionality that do not add value—and drive teams toward peak performance through continuous learning and collaborative execution.
Continuously enhance the engineering operating model to be lean, adaptable, and responsive—guiding and transforming the organization to embrace lean principles and foster a culture of innovation.
Establish and evolve reference architectures, coding standards, and engineering and quality benchmarks that ensure robust, secure, scalable, and reliable/resilient solutions.
Ensure appropriate, responsible technology adoption—developing explainable, scalable, reliable, and secure AI and agentic products—and proactively identify technical risks, developing mitigation strategies through proactive problem-solving and contingency planning.
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.
Engage stakeholders at all levels—from team members to middle management to executives—building collaborative, constructive relationships and co-creating momentum and value across multiple organizational levels.