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About This Role
Associate Director, Applied AI Engineering
Role Overview: 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.
Key Responsibilities:
- Strategic Vision and Alignment: 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. 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 to engineering teams and business stakeholders. Ensure the organization is well\-informed about objectives, KPIs, maturity, compliance, and progress. Promote a culture of reuse, quality, and speed\-keeping an eye on leverage of existing assets and on the inference, token, and cloud cost of what we build, to maximize outcomes and minimize total cost.
- 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 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.
- Capability Evolution and Development: As a recognized engineering leader, 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. Cultivate a growth mindset and modern engineering behaviors across the organization.
- Iterative Value Delivery: 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.
- 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\-features and functionality that do not add value\-and drive teams toward peak performance through continuous learning and collaborative execution.
- 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 across the product lifecycle. 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.
- Tech/Quality Risk Management: 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.
- 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 middle management to executives\-building collaborative, constructive relationships and co\-creating momentum and value across multiple organizational levels.
The team: US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost\-effective model that focuses on value/outcomes that leverages 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. It is the engine that drives Deloitte, serving 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.
The successful candidate will possess:
- Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.
Required Qualifications:
- 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.
- 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
Other:
- Limited immigration sponsorship may be available.
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $130900 to $268700\.
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.
EA\_ExpHire
Salary Context
This $130K-$268K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Deloitte, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($199K) sits 7% below the category median. Disclosed range: $130K to $268K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Deloitte AI Hiring
Deloitte has 59 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Engineer, Research Engineer. Positions span Rosslyn, VA, US, Baltimore, MD, US, Morristown, NJ, US. Compensation range: $140K - $379K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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