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About This Role
Are you someone who challenges the status quo, acts autonomously, and has experience leading platform and knowledge engineering work in broad\-scale transformation programs? Are you ready to take the next leap in your career by leveraging your experience to convert a legacy software development process into one led by agentic models? Are you ready to build the tooling and knowledge infrastructure that other engineering teams will depend on every day? If the answer to all of the above questions is "Yes," come join the world's leading professional services firm. If you are prepared and poised to take the next step in your career, you can help build the platform that powers agentic software delivery across a global business. Then we want to talk to you.
Work you'll do
As a Manager, Agentic Engineering Platform, you will build and lead a small, high\-leverage engineering team responsible for the infrastructure that underpins our agentic software development lifecycle. You will take a hands\-on approach to designing and building the LLM Wiki and AWS Bedrock\-managed knowledge bases that serve as steering content for agentic development tools. You will own the solution architecture behind these systems, applying agentic AI patterns such as retrieval\-augmented generation, tool calling, and stateful workflows, and building in LLM evaluation, observability, and cloud\-native deployment from the outset, all while keeping the platform aligned with enterprise architecture, security, compliance, and operational standards. You will build integrations between software development tools such as AWS Kiro and Azure DevOps, so that knowledge captured in ADO wikis becomes steering content, and so that agentic tools can read program artifacts such as epics, features, and user stories to generate code directly from approved requirements. You will also build integrations between ServiceNow and Azure DevOps that create defects automatically and route pull request assignments to the right developers. Beyond these integrations, you will deliver tools, utilities, and workflows that help technical program managers identify risk and cross\-project dependencies by intelligently processing information across the platforms and tools our programs already use. The ideal candidate is a dependable team player and mentor who can move fluidly between hands\-on engineering and defining the technical direction of a growing platform. Key responsibilities include the following:
- Platform Ownership: Lead the architecture, build, and ongoing support of the LLM Wiki, AWS Bedrock\-managed knowledge bases, and related steering content that power agentic development tools.
- Agentic Solution Architecture: Design scalable, secure, and maintainable AI solutions using retrieval\-augmented generation, tool calling, and stateful workflows, with LLM evaluation, observability, and cloud\-native deployment built in.
- Enterprise Alignment and Governance: Define service boundaries and integration patterns, and ensure the platform aligns with enterprise architecture, security, compliance, and operational standards.
- Tool Integrations and Automation: Build and maintain integrations across AWS Kiro, Azure DevOps, and ServiceNow so program artifacts, defects, and pull request workflows move automatically through the platform.
- Risk and Dependency Enablement: Deliver tools and workflows that help technical program managers identify risk, dependencies, and delivery issues across platforms.
A successful candidate would possess these skills:
- Strong collaboration and relationship\-building skills
- Clear, concise communication with technical and non\-technical audiences
- Adaptability and a strong bias for learning and continuous improvement
The team
At Deloitte Tax LLP, our Product Engineering team within Global Employer Services (GES) Technology, has modernized software and product delivery, creating a scalable, cost\-effective model that focuses on value and outcomes through 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. Within Product Engineering, this team owns the agentic development platform: the LLM Wiki, AWS Bedrock\-managed knowledge bases, and the integrations that connect our software development tools, program management systems, and ticketing platforms. Engineering teams across the organization depend on the steering files, knowledge bases, and integrations this team builds and maintains to run their agentic development workflows.
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, information technology, software engineering, or a related field.
- 5\+ years of experience developing and deploying solutions using modern programming languages and cloud\-native platforms (for example, Python, Node.js, TypeScript, SQL/NoSQL, AWS).
- Strong solution design and architecture capabilities, with experience in Python agentic AI patterns, LLM evaluation patterns, retrieval\-augmented generation, tool calling, stateful workflows, observability, and cloud\-native deployment.
- Ability to design scalable, secure, maintainable, and reusable AI solutions; define service boundaries and integration patterns; establish evaluation and reliability frameworks; and ensure alignment with enterprise architecture, security, compliance, and operational standards.
- Hands\-on experience building or integrating with large language model tooling, including retrieval\-augmented generation, managed knowledge bases, and prompt or steering\-content design (AWS Bedrock or equivalent).
- Demonstrated experience building integrations between enterprise development, program management, and ticketing platforms (for example, Azure DevOps, ServiceNow, Jira, Confluence) using their APIs.
- Demonstrated ability to design and build tools, utilities, or workflows that process and correlate information across multiple platforms.
- Proven ability to implement and maintain automated CI/CD pipelines and quality assurance practices within Agile development environments.
- Limited immigration sponsorship may be available.
- Ability to travel up to 10%, on average, based on the work you do and the clients and industries/sectors you serve.
- 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 Information Systems Security Professional (CISSP)
- Certified SAFe® Agile Software Engineer
- Certified SAFe® DevOps Practitioner
- ISTQB (International Software Testing Qualifications Board)
- Microsoft Azure
- Microsoft Certified Solutions Developer (MCSD)
- Oracle Certified Professional
Preferred:
- Direct experience with agentic development tools such as AWS Kiro, GitHub Copilot, or similar, including designing steering files or context that these tools consume.
- Experience architecting knowledge bases for retrieval\-augmented generation, including chunking strategy, embedding selection, and relevance tuning.
- Experience with Azure DevOps and ServiceNow APIs, webhooks, or connectors, including building bidirectional automation between the two.
- Prior experience leading or building a platform engineering team that treats internal engineering teams as its customers.
- Prior transformation or modernization experience, leading a team as a technical lead through a shift in development methodology.
- Skilled at translating technical program management needs, such as risk and dependency tracking, into working software
- Track record of effective code reviews, ensuring code quality, security, and alignment with best practices.
- Strong written and verbal communication skills, evidenced by experience collaborating across technical and non\-technical teams.
- Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care.
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 $137,700 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 $137K-$261K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($199K) sits 7% below the category median. Disclosed range: $137K to $261K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Deloitte AI Hiring
Deloitte has 59 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Engineer, Research Engineer. Positions span Rosslyn, VA, US, Baltimore, MD, US, Morristown, NJ, US. Compensation range: $140K - $379K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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.
Frequently Asked Questions
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