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About This Role
As an Associate Director, Applied AI Platform Engineering , you will set the engineering vision and technical direction for the Applied AI engineering platform-the frameworks, tooling, control plane, and data platform that every other engineering team builds on-treating the platform as a product and solving recurring problems once, well, for many teams. Leading across teams and product groups, you will stay hands-on in your craft-shaping architecture, design, and code-while driving the golden paths, reusable components, and governed, self-service capabilities that engineering teams depend on. Your leadership will be pivotal in delivering tangible leverage and value across Deloitte's AI engineering investments, maximizing reuse and reducing per-team toil, and advancing Applied AI platform engineering across the organization.
You will bring extensive engineering craftsmanship and deep expertise across platform engineering, distributed systems, and modern AI/ML and data infrastructure, together with an exemplary track record of high-quality, reusable, 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.
Work You'll Do:
- Strategic Vision and Alignment: Craft and articulate a vision for the Applied AI engineering platform-the frameworks, tooling, accelerators, control plane, and data platform that every other engineering team builds on-in alignment with the Business Strategy and US Deloitte Technology strategy. Treat the platform as a product, mapping the needs of consuming engineering teams to reusable, self-service capabilities. Collaborate with diverse stakeholders across product, engineering, SRE, security and risk, data governance, and infrastructure at all organizational levels.
- Advocacy and Technology Roadmap: Advocate for, develop, and communicate the integrated platform engineering strategy and its implementation roadmap, positioning the platform as a product to the engineering teams who consume it and to business stakeholders. Ensure the organization is well-informed about objectives, KPIs, adoption, maturity, compliance, and progress. Promote a culture of reuse, quality, and speed-solving recurring problems once, well, for many teams to reduce per-team build and operate toil, maximize engineering leverage, and minimize total cost of ownership.
- Craft Mastery and Objectives Realization: Define, measure, and drive the achievement of platform KPIs and NFRs-adoption, reliability-by-design, developer experience, performance, scalability, and security. Establish and evolve platform reference architectures, golden paths, and reusable components-including the AI control plane (with the model/LLM gateway) and agent-runtime patterns, MLOps/LLMOps tooling, model serving, retrieval and vector infrastructure and RAG-as-a-Service, Eval-as-a-Service and observability tooling, prompt management, and feature store; governance-as-code enforcement, control codification, and ontology management; the data platform (data catalog and marketplace, DataOps, data management and enterprise integration, master data, pipelines, and metadata/lineage); and CI/CD at platform scale-delivered through the AI and Agentic SSDLC as self-service, governed capabilities with full automation from discovery to production to operations and quality checks through the SSDLC lifecycle. Remain hands-on with design, architecture, and code-contributing to the leverage and velocity of the engineering teams and product groups that build on the platform-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 platform engineering practices-internal developer platforms, distributed systems, the AI control plane and agentic patterns, MLOps/LLMOps, cloud-native design and container orchestration, platform-scale infrastructure-as-code, and the data platform. 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 platform engineering, delivering platform capabilities as lean, adoption-validated increments rather than big-bang builds. Apply a leaning-forward approach and empirical methods to navigate complexity and uncertainty, keeping capabilities supportable and maintainable and ensuring each increment delivers value and stays aligned with consuming-team and business goals.
- Customer-Centric Problem Solving: Maintain a relentless focus on the real needs of the engineering teams who consume the platform-your primary customers-developing lean capabilities through rapid, inexpensive experimentation and engaging those teams before, during, and after delivery so the right capability is delivered at the right time and adopted, not shelved. Minimize unnecessary technical complexity and avoid overengineering, and drive toward peak performance through continuous learning and collaborative execution.
- Expert Proficiency and Continuous Improvement: Possess deep expertise in modern platform engineering and AI/ML and data infrastructure, with a keen ability to identify inefficiencies and opportunities for innovation across the platform and the engineering operating model. Continuously enhance the 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 the enterprise tech-stack conformance baseline, platform standards, and quality benchmarks that engineering teams build against-embedding consistency, compliance, and security by default through guardrails (PII redaction, prompt-injection and content controls, tool permissioning and RBAC) and multi-tenant isolation, so that the secure, compliant, and reliable path is the easy path. Build capabilities that are operable, instrumented, performant, and drift-resistant by design to the production standards set by SRE, delivering them admissible-by-design and co-defining service-level objectives with SRE, who own the admission decision. Proactively identify technical risks and develop 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-and collaborate with empowered, cross-functional partners across engineering, SRE, security and risk, and data governance, integrating their constraints into the paved roads. Build collaborative, constructive relationships and co-create momentum and value across the organization
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.
Qualifications
Required:
- A bachelor's degree in computer science, software engineering, data science, machine learning, or related discipline. Experience is the most relevant factor.
- 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.
- 10+ years of software and platform engineering experience with most of the following: Angular, React, NodeJS, Python(Mandatory), , C#, .NET, Java, Rust, SQL/NoSQL, REST/SOAP/GraphQL, SSO/MFA, PyTorch, TensorFlow, LangChain, LangGraph, LangSmith, LangFuse, Terraform, as well as unit, integration, and end-to-end testing frameworks & tools, specifically BDD, Gherkin, Cucumber, Playwright, and Selenium.
- 7+ years of experience architecting and delivering enterprise solutions and platforms 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, as well as container orchestration (Kubernetes, Docker), Big Data, data platform tooling such as Informatica, Databricks, or Delphix (spanning data governance and observability, data fabric, and data anonymization), CI/CD and infrastructure-as-code at platform scale, and distributed systems.
- 5+ years of experience designing, building, and operating AI/ML platform or infrastructure, with hands-on experience across building tooling for MLOps/LLMOps, model serving, retrieval and vector infrastructure, and eval/observability instrumentation for LLM integration (OpenAI, Anthropic, or open-source models).
- 2+ years of experience establishing engineering platform standards and golden paths, including actively leading, mentoring, and guiding team members in the adoption and continuous improvement of these standards, treating the platform as a product.
- Prior experience with AI control-plane and agent-runtime patterns: model/LLM gateway, A2A and MCP integration, agent runtimes (e.g., Google ADK, Amazon Bedrock AgentCore), guardrails (PII redaction, prompt-injection, content, tool permissioning/tool-RBAC), policy-as-code, and multi-tenant isolation.
- Prior experience with enterprise data platform engineering: data pipelines, self-service and data-product enablement, governance-as-code enforcement, and metadata/lineage.
- 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, and ArgoCD to deliver high-quality platforms and products rapidly.
- Ability to travel 10%, on average, based on the work you do and products you build.
- 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 $130,900 to $268,700
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.
Salary Context
This $130K-$268K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($199K) sits 6% below the category median. Disclosed range: $130K to $268K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Deloitte AI Hiring
Deloitte has 128 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Safety, AI Product Manager. Positions span Jacksonville, FL, Morristown, NJ, US, New York, NY. Compensation range: $118K - $338K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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