Interested in this AI/ML Engineer role at Deloitte?
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About This Role
What You'll Do
As a Deloitte Tax, AI Engineer Manager, you will oversee the design, development, deployment, and support of custom AI applications and modules to address key business needs. You will lead a team of engineers, drive project execution, manage stakeholder communications, and ensure high\-quality deliverables aligned with organizational objectives.
Recruiting for this role ends on May 31, 2027\.
Responsibilities:
- Define and enforce best practices and coding standards across the project.
- Conduct thorough code reviews to ensure adherence to established guidelines and maintain high code quality.
- Working both independently and in close collaboration with others in the team
- Communicating clear instructions to team members and help manage the flow of day\-to\-day operations
- Communicating with the client on a regular basis
- Design, develop, and maintain robust and scalable Python applications.
- Write clean, maintainable, and efficient code following best practices and coding standards.
- Optimize code for performance and scalability, ensuring efficient data handling.
- Work closely with cross\-functional teams to deliver high\-quality software solutions.
- Identify and resolve technical issues, ensuring the reliability and performance of applications.
- Create and maintain comprehensive documentation for code, processes, and workflows.
- Provide guidance and mentorship to junior developers, fostering a collaborative and productive team environment.
The Team
The prospective team you will be working with is responsible for the design, development, and deployment of innovative, enterprise technology, tools, and standard processes to support the delivery of tax services. The team focuses on the ability to deliver comprehensive, value\-added, and efficient tax services to our clients. It is a dynamic team with professionals of varying backgrounds from tax technical, technology development, change management, and project management. The team consults and executes on a wide range of initiatives involving process and tool development and implementation including training development, engagement management, tool design, and implementation.
Qualifications
Required:
- Ability to perform job responsibilities within a hybrid work model that requires US Tax professionals to co\-locate in person 2 \- 3 days per week.
- Bachelor's degree in computer science, engineering, or a relevant discipline.
- 5\+ years of experience in development with demonstrated experience designing, developing, and maintaining robust Python applications.
- Hands\-on experience in web development with FastAPI, including Pydantic for data validation/schema definition.
- Proven skills in asynchronous and parallel programming with practical experience using asyncio.
- Experience working in Agile environments and applying core design patterns.
- Demonstrated proficiency in .NET Core, ASP.NET Core Web API, and databases (SQL/NoSQL, Entity Framework 6\+).
- Ability to travel up to 10%, on average, based on the work you do and the clients and industries/sectors you serve.
- Limited immigration sponsorship may be available.
- One of the following active accreditations obtained:
- + Licensed CPA in state of practice/primary office if eligible to sit for the CPA
+ If not CPA eligible:
+ - Licensed Attorney
- Enrolled Agent
- Technology Certifications:
- * AWS Certified Solutions Architect
- Certified in Risk and Information Systems Controls (CRISC)
- Certified Information Systems Security Professional (CISSP)
- Certified SAFe® Advanced Scrum Master
- Certified SAFe® Agile Software Engineer
- Certified SAFe® Agilist
- Certified SAFe® Architect
- Certified SAFe® DevOps Practitioner
- Certified SAFe® Scrum Master
- Certified Scrum Developer (CSD)
- Certified Secure Software Lifecycle Professional (CSSLP)
- Certified Secure Software Lifecycle Professional (CSSLP) \- (ISC)2
- Microsoft Azure
Preferred:
- Experience with LLMs (Large Language Models) in solving real\-world problems and building agentic AI applications.
- Experience with agentic frameworks such as LangGraph.
- Advanced prompt engineering knowledge for LLM optimization.
- Exposure to multi\-modal Gen AI models (text\-image, text\-audio, etc.).
- Familiarity with Retrieval\-Augmented Generation (RAG) pipelines and vector databases/hybrid search.
- Experience with performance tuning, reusable library creation, and advanced troubleshooting.
- Familiarity with modern front\-end technologies (Angular), MongoDB, NPM, and Azure DevOps Build/Release configuration.
- Knowledge on Angular, Mongo DB, NPM and Azure Devops Build/Release configuration.
- Strong verbal and written communication skills; strong listening, interpersonal, and facilitation skills.
- Self\-starter with solid analytical and problem\-solving skills.
- Practical and pragmatic approach to balancing standardized processes with flexibility to meet project goals effectively.
- Excellent organizational skills with the ability to self\-manage, prioritize tasks, structure workload, and meet tight deadlines.
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 $128,025 to $261,625\.
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 $128K-$261K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($194K) sits 8% above the category median. Disclosed range: $128K to $261K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Deloitte AI Hiring
Deloitte has 77 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, Research Engineer. Positions span Stamford, CT, US, Austin, TX, US, Jersey City, NJ, US. Compensation range: $121K - $372K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.
Frequently Asked Questions
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