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About This Role
Are you someone who wants hands\-on experience building the tooling and knowledge infrastructure behind an agentic software development lifecycle? Are you ready to take the next step in your career by building integrations and knowledge bases that other engineering teams will depend on every day? Are you ready to work alongside experienced engineers on a small, high\-visibility platform team? 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 Senior Consultant, Agentic Engineering Platform, you will work alongside the team's Manager and other engineers to build the infrastructure that underpins our agentic software development lifecycle. Day to day, you will write Python to build APIs, automate workflows, and clean and structure the data (both structured and unstructured) that feeds our knowledge bases and tooling. You will build retrieval\-augmented generation pipelines against vector databases, using embeddings and evaluation techniques to keep the LLM Wiki and AWS Bedrock\-managed knowledge bases accurate and grounded. You will use LangChain, LangGraph, or a comparable orchestration framework to build the agents and stateful workflows that let AWS Kiro read program artifacts such as epics, features, and user stories out of Azure DevOps and turn them into working code. You will also help build integrations between ServiceNow and Azure DevOps that create defects automatically and route pull request assignments to the right developers, and you will build lightweight interfaces, using tools such as Streamlit, Gradio, or React, so engineers and technical program managers can use what you build without touching raw APIs. Beyond these integrations, you will build tools, utilities, and workflows, drawing on pandas, NumPy, and SQL, that help technical program managers identify risk and cross\-project dependencies by processing information across the platforms our programs already use. The ideal candidate is a dependable team player who is eager to grow their engineering craft while contributing directly to a platform other teams rely on. Core responsibilities of the role include:
- Build data and knowledge foundations that prepare structured and unstructured information for the LLM Wiki and AWS Bedrock\-managed knowledge bases.
- Engineer grounded AI capabilities by developing retrieval\-augmented generation pipelines, agentic workflows, evaluation harnesses, and guardrails that improve accuracy, reliability, and safety.
- Connect the agentic software development lifecycle by integrating AWS Kiro, Azure DevOps, ServiceNow, and related platforms to automate the flow of program artifacts, defects, pull requests, and code updates.
- Develop production\-ready platform capabilities including backend services, APIs, lightweight user interfaces, deployment pipelines, logging, and monitoring that enable teams to use tools without direct API access.
- Deliver iterative, well\-documented solutions that improve visibility into program risks and dependencies, reduce manual effort, and help engineering and technical program management teams adopt and extend the platform.
A successful candidate would possess these skills:
- Communicates clearly and confidently with technical and non\-technical audiences, translating complex ideas, trade\-offs, and priorities into simple language.
- Collaborates effectively across teams by building trust, aligning stakeholders, and moving work forward in a fast\-paced, cross\-functional environment.
- Shows initiative and sound judgment by staying organized, adapting quickly to changing needs, and delivering high\-quality work with minimal direction.
The team
At Deloitte Tax LLP, our Global Employer Services (GES) Technology Group consultants help multinational clients develop programs, processes and digital offerings to manage a global workforce and the compliance obligations arising from global mobility, business travel and remote working. People within our technology group come from a diverse background \- they partner with our go\-to\-market teams and global clients to solve challenging problems and design experiences and products that anticipate what our Deloitte clients will want and solutions that keep them compliant with global and local regulations.
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, engineering, or a related field.
- 3\+ years of hands\-on Python experience, including building APIs, writing automated tests, debugging, and scripting for automation.
- Experience with SQL and data preprocessing, including data cleaning, feature handling, and working with both structured and unstructured data.
- Working knowledge of core data science libraries such as pandas, NumPy, and scikit\-learn, with basic exposure to visualization libraries such as Matplotlib or Seaborn for analysis and evaluation.
- Solid understanding of LLM and generative AI fundamentals, including prompting, embeddings, retrieval\-augmented generation, vector databases, evaluation methods, guardrails, and hallucination management.
- Experience building chains, tools, or agents with LangChain, LangGraph, or a comparable orchestration framework for stateful AI workflows.
- Experience using AWS services programmatically via Boto3, including S3, Lambda, Bedrock, DynamoDB, and Agent Core, to support deployment workflows.
- Experience with API integrations across at least two enterprise platforms (for example, Azure DevOps, ServiceNow, Jira, Confluence).
- Experience with backend deployment practices, including FastAPI, Docker, CI/CD, logging, and monitoring.
- Limited immigration sponsorship may be available.
- Ability to travel up to 20%, on average, based on the work you do and the clients and industries/sectors you serve.
- One of the following active accreditations obtained in process, or willing/able to obtain:
- + Licensed CPA in state of practice/primary office if eligible to sit for the CPA
+ If not CPA eligible:
+ - Licensed attorney
- Enrolled Agent
- 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:
- Experience building usable interfaces for AI\-powered tools using Streamlit, Gradio, React, or similar front\-end frameworks
- Familiarity with LLM evaluation and AI development workflows using tools such as DeepEval, RAGAS, AWS Kiro, GitHub Copilot, Claude Code, or similar.
- Experience integrating platforms and automation workflows using Azure DevOps and ServiceNow APIs, webhooks, or connectors.
- Prior experience supporting internal engineering teams or transformation efforts, including modernization initiatives and translating technical program management needs such as risk and dependency tracking into working software.
- Strong software engineering and collaboration skills, including writing clean, well\-tested code, contributing constructively in code reviews, and communicating effectively across technical and non\-technical teams.
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 $102,750 to $195,250\.
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 $102K-$195K range is below 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($149K) sits 31% below the category median. Disclosed range: $102K to $195K.
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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