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About This Role
Today's chief financial officers (CFOs) and supply chain executives are being asked to improve business performance and shareholder value, along with operational effectiveness and efficiency. Deloitte Oracle Generative AI Architect Managers help clients delineate strategy and vision, design and implement process and systems which align with business objectives and have a measurable impact on growth. Do you want to be a part of a team that transforms the business landscape for its clients? Do you want to be on the winning team that drives transformation, improves productivity, and streamlines business operations with AI? Do you feel your skills surpass those of your peers and colleagues? If your answer is yes to all these questions, it's very nice to meet you and we want to hear from you immediately!
Recruiting for this role ends on 08/31/2026\.
Work you'll do
As an Oracle Gen AI Architect Manager on the Oracle team, you will translate business requirements into scalable AI solutions using Oracle Cloud Infrastructure services and modern AI frameworks. You will help organizations improve automation, insight, and operational efficiency while applying emerging technologies such as Microsoft Copilot, Google Cloud Agent Space, LangGraph, Glean, and other enterprise AI platforms to drive business value.
- Develop and align enterprise AI architecture on OCI, with a focus on generative AI capabilities and Oracle applications
- Architect and deliver integrated AI solutions, including agentic workflows, retrieval\-augmented generation pipelines, and enterprise platform integrations
- Define and enforce governance, security, compliance, and architecture standards for artificial intelligence and machine learning initiatives
- Identify high\-value AI use cases and guide teams on prompt engineering, model selection, and model optimization
- Collaborate with stakeholders, mentor delivery teams, lead technical reviews, and support pilot deployments that drive business outcomes
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 Oracle offering drives business transformation services to improve performance and value delivered by the full suite of Oracle solutions.
Got your head in the cloud? With so much technology moving to the Cloud, our business requirements are taking us to new heights. By harnessing the power of Oracle ERP Cloud, you can streamline enterprise business processes with ERP Cloud's Financials, Procurement, or Portfolio Management. Do you have the ability to transform an organization through the latest social, mobile, and analytic technologies? We're looking for someone that can increase the effectiveness of decision making and drive innovation. If your head is in the cloud, find out where we can take you with Oracle Enterprise Solutions. Learn more about our Oracle practice.
Qualifications
Required:
- Bachelor's degree or higher in Computer Science, Information Technology, Software Engineering, or a related field
- 8\+ years of experience in enterprise architecture, including 2\+ years of experience in artificial intelligence, machine learning, or generative artificial intelligence solution architecture
- Experience with Oracle Cloud Infrastructure services, including Generative AI Service, Functions, API Gateway, Data Science, Autonomous Database, and Oracle Integration Cloud
- Experience designing solutions using large language models, embeddings, retrieval\-augmented generation, multi\-agent systems, and integrations with enterprise applications
- Experience with security, compliance, and governance in cloud and enterprise environments, and experience with Python, Structured Query Language, REST APIs, and cloud\-native architecture patterns
- Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve.
- Limited immigration sponsorship may be available.
Preferred:
- Experience with Oracle Fusion AI Studio and Redwood UI extensions
- Experience with Oracle ERP Cloud modules, including Financials, supply chain management, Procurement, and human capital management, or with Oracle E\-Business Suite, JD Edwards, or PeopleSoft
- Experience with enterprise AI platforms such as Glean, Moveworks, or similar enterprise search and productivity AI platforms
- Experience working across OCI and at least one additional cloud platform, including Amazon Web Services, Microsoft Azure, or Google Cloud Platform
- Experience with LangChain, LangGraph, NVIDIA NIM, or Hugging Face
- Experience leading AI or ERP transformation programs for large enterprises
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 $134,500 to $265,100\.
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 $134K-$265K range is above the median 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. Mid-level AI roles across all categories have a median of $165,778. This role's midpoint ($199K) sits 8% above the category median. Disclosed range: $134K to $265K.
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
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national 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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