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About This Role
Step into a role where you'll help shape the future of finance. As part of our Finance Transformation team, you'll collaborate with CFOs, finance leaders, and executive clients to solve their most complex challenges. Leveraging Deloitte's unmatched resources and deep industry insights, you'll deliver innovative, market\-driven solutions that modernize finance functions, elevate performance, and drive meaningful organizational change. Join us to empower clients to become strategic business partners, manage risk, and unlock new levels of financial and operational excellence.
As a Senior Manager and AI/Automation Architect within our Finance Transformation Tech Enablement team, you will own the technical vision for our unified Finance Operate Platform. You will make definitive build\-versus\-buy decisions across agentic AI, automation, and integration layers, and lead client\-facing conversations on delivery strategy. This is a senior leadership role that shapes how Deloitte designs and delivers technology\-enabled Finance Operate engagements at scale across Controllership, Treasury, and FP\&A.
Recruiting for this role ends on 09/30/2026\.
Work you'll do
As a Senior Manager \- AI / Automation Architect on the Finance Transformation Tech Enablement team, you will be responsible for:
Technical Vision \& Architecture
- Own the end\-to\-end technical architecture for the Finance Operate Platform, spanning across our overall consulting wide delivery platform, offering delivery portal, and sub\-offering accelerators.
- Define and enforce build\-versus\-buy decisions across agentic AI, RPA, workflow automation, data/analytics, and alliance integrations.
- Lead design of multi\-agent AI architectures, LLM/RAG pipelines, and agentic workflow orchestration aligned to Finance Operate use cases.
- Establish reusable patterns, technical standards, and guardrails that can be applied consistently across Controllership, Treasury, and FP\&A sub\-offerings.
Execution Leadership
- Lead internal stakeholder and client conversations on agentic delivery strategy, platform capabilities, and technology roadmap (primarily for internal firm leadership with some nuances for client facing conversations).
- Partner with offshore Solution Architect and cross\-functional teams to translate platform strategy into executable delivery plans.
- Provide technical oversight across concurrent workstreams; identify and resolve architectural dependencies and delivery risks proactively.
- Guide Agile/Scaled Agile planning and engineering practices across the Tech Enablement team.
Platform \& Innovation
- Evaluate emerging AI/ML, agentic framework, and automation technologies and make recommendations for adoption within the Finance Operate service catalog.
- Drive development and operationalization of AI\-powered POCs demonstrating value in finance service automation, including journal entry validation, reconciliation, forecasting, and exception management.
- Collaborate with Consulting delivery platform team leads on the Agentic Layer roadmap (Phase 1 ETL/data readiness and Phase 2 business process automation).
Team \& Stakeholder Engagement
- Mentor and develop engineers, consultants, and architects across the team.
- Present technical strategies and roadmap updates to senior internal and client stakeholders.
- Build trusted\-advisor relationships with Finance Operate offering leadership and delivery teams.
A 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 Finance Operate offering provides ongoing operation of the Finance function as an extension of Deloitte's Finance Transformation Advise and Implement offering portfolio. The team works across finance strategy, process, operations, and technology to help organizations enable scalable finance outcomes. Professionals in this practice support delivery in complex environments while helping clients advance modernization across Finance Operate capabilities.
Qualifications
Required:
- Bachelor's degree in Computer Science, Engineering, Business, or related field (advanced degree preferred).
- 10\+ years of progressive technology delivery experience, including 3\+ years in a senior architecture or technical leadership role.
- Demonstrated expertise in designing and deploying agentic AI, LLM\-driven automation, and multi\-agent systems in enterprise environments.
- Deep proficiency in Python, AI/ML model development, and workflow orchestration (LangChain, LangGraph, or similar frameworks).
- Experience with cloud platforms (Azure, AWS, or GCP) for deploying AI and automation workloads.
- Strong knowledge of integration architecture, APIs, microservices, and CI/CD practices.
- Proven track record leading client\-facing technical conversations and executive stakeholder engagement.
- Experience with Finance or ERP system contexts (Record to Report, P2P, Treasury, or FP\&A) preferred.
- Ability to travel 25%, on average, based on the work you do and the clients and industries/sectors you serve
- Limited immigration sponsorship may be available
Preferred:
- Familiarity with ServiceNow workflow architecture and Finance Operate delivery platforms.
- Experience with data/analytics platforms such as Databricks, Alteryx, or similar.
- Knowledge of RPA platforms (UiPath, Automation Anywhere, Blue Prism).
- Certifications in cloud architecture, AI/ML, or Agile/SAFe frameworks.
- Familiarity with RAG architectures, vector databases, and agentic frameworks such as AutoGPT.
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 $163,000 to $322,100\.
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 $163K-$322K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($242K) sits 31% above the category median. Disclosed range: $163K to $322K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Deloitte AI Hiring
Deloitte has 69 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Consultant, Data Scientist. Positions span Baltimore, MD, US, Jersey City, NJ, US, Stamford, CT, US. Compensation range: $140K - $372K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, 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 Safety roles lead at $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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