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About This Role
Technical Manager \- AI \& Data Risk Management \- Financial Services
Our Deloitte Regulatory, Risk \& Forensic team helps client leaders translate multifaceted risk and an evolving regulatory environment into defensible actions that strengthen, protect, and transform their organization. Join our team and use advanced data, AI, and emerging technologies with industry insights to help clients bring clarity from complexity and accelerate their path to value creation.
Work you'll do
The Manager is a key hands\-on leader of a team providing data and analytics solutions to our clients, and you will have following roles:
- Act as a team leader in engagements, providing project oversight and guidance on the direction for the team ensuring the team is aligned with client / engagement objectives
- Be the main point of contact for clients, communicating progress, and insights to stakeholders
- Design and implement solutions to enhance data management capabilities such as data architecture, data platforms, data governance, data quality, and data security
- Apply knowledge of Financial Services industry systems, tools, and technologies to develop robust and scalable solutions
- Integrate and/or pilot next\-generation technologies such as cloud platforms, machine learning and Generative AI (GenAI) in designing solutions or execution of engagements
- Oversee technical components of data initiatives and perform technical reviews of solutions designed by the team
- Coordinate with cross\-functional teams internally and externally to execute engagements
- Develop strategies for utilizing analytics and visualization tools to transform raw data into actionable insights for the clients
- Prepare reports for senior management that detail progress, challenges, and milestones related to projects and initiatives
- Present findings and recommendations to stakeholders in a clear, impactful manner
The successful candidate would possess these skills:
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast\-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to mentor and provide clear guidance to others
The team
Our Regulatory \& Financial Risk offering supports clients' business priorities, balancing risk and regulatory requirements with enhancing business value and optimizing outcomes. We deliver enhanced value through strategic transformation, end\-to\-end implementation, and focus on business\-as\-usual sustainability across processes, controls, and data \& analytics infrastructure.
Required Qualifications:
- Bachelor's degree in Computer Science, Data Science, Information Technology, Information Systems, or a related field
- 7\+ years' experience in data architecture, data management, or data engineering
- 3 years' experience in the financial services or fintech industry
- Proficiency in tools in the following areas:
- + Scripting and Programming (e.g., Python, SQL, R, Java, Scala, etc.)
+ Big Data Tools (e.g., Databricks, Snowflake, Apache Spark \& Kafka, etc.)
+ Data Management (e.g., Collibra, Alation, Informatica, Alteryx, etc.)
+ Predictive Analytics (e.g., Python, IBM SPSS, SAS Enterprise Miner, RPL, Matl, etc.)
+ Data Visualization (e.g., Tableau, PowerBI, Qlik, etc.)
+ Data Mining (e.g., Microsoft SQL Server, Amazon Redshift, Google BigQuery, etc.)
- Experience in:
- + Reporting tools such as Axiom for regulatory reporting in banking
+ Data integration and migration projects within financial services
+ Data governance tools and their application in a regulatory context
- Ability to manage large datasets and perform complex data manipulations and analyses
- Strong analytical skills with the ability to interpret complex data and translate it into actionable insights
- Proven track record of effective communication (written and verbal) and complex problem solving in fast paced environments
- Experience managing distributed teams across geographies and overseeing complex projects
- Limited immigration sponsorship may be available
- Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve
Preferred Qualifications:
- Master's degree in Computer Science, Information Technology, Information Systems or a related field OR MBA
- Project Management Professional (PMP), Cloud Certification (e.g. AWS, Azure), Certified Data Management Professional (CDMP), or similar credentials
- 4\+ years of experience in the financial services industry
Information for applicants with a need for accommodation: https://www2\.deloitte.com/us/en/pages/careers/articles/join\-deloitte\-assistance\-for\-disabled\-applicants.html
For individuals assigned and/or hired to work in Boston , Deloitte is required by law to include a reasonable estimate of the compensation range for this role. This compensation range is specific to Boston and 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. 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 $134500 to $265100
Salary Context
This $134K-$265K 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 ($199K) sits 10% above the category median. Disclosed range: $134K to $265K.
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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