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About This Role
Our Deloitte AI \& Engineering team works to transform technology platforms, drive innovation, and help make a significant impact on our clients' success. You'll work alongside talented professionals reimagining and reengineering operations and processes that are critical to businesses. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.
Work You'll Do
As a Lead AI and Data Science Engineer II on the team, you will be responsible for:
- Lead the design, development, testing, and deployment of machine learning and artificial intelligence solutions for business and client use cases.
- Manage AI engineering workstreams by assigning work, reviewing deliverables, and driving quality, timeliness, and issue resolution across the team.
- Develop and oversee data pipelines, model training workflows, and production\-grade application components that support AI\-enabled products.
- Collaborate with engineers, data scientists, product stakeholders, and business teams to translate requirements into scalable technical solutions and guide technical decisions.
- Coach and mentor junior practitioners, provide day\-to\-day guidance, and monitor model and application performance to improve accuracy, reliability, and scalability.
Skills for Success* 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
Deloitte's Government \& Public Services (GPS) practice\-our people, ideas, technology, and outcomes\-is designed for impact. Serving federal, state, and local government clients as well as public higher education institutions, our team of professionals brings fresh perspective to help clients anticipate disruption, reimagine the possible, and fulfill their mission promise.
Our AI \& Data offering provides a full spectrum of solutions for designing, developing, and operating cutting\-edge data and AI platforms, products, insights, and services. Our offerings help clients innovate, enhance, and operate their data, AI, and analytics capabilities, ensuring they can mature and scale effectively with organizational intelligence programs and differentiated strategies to win in their chosen markets.
Qualifications Required* Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field.
- 7\+ years of professional experience.
- 5\+ years of experience designing, developing, or deploying machine learning, artificial intelligence, or advanced analytics solutions.
- 3\+ years of experience programming in Python, Pyspark, Pytorch, and Tensorflow.
- 2\+ years of technology consulting experience in the State Government or Local Government space.
- 2\+ years of experience leading technical teams, projects, or workstreams supporting artificial intelligence or machine learning solution delivery.
- 1\+ years of experience working with AI technologies such as Gemini, Anthropic, and OpenAI.
- Active certification or advanced certification in Python, Pyspark, Pytorch, and Tensorflow.
- Ability to travel 20%, on average, based on the work you do and the clients and industries/sectors you serve.
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.
Preferred
- Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field.
- Active US Government security clearance.
- 2\+ years of experience deploying machine learning models into production environments.
- 2\+ years of experience working with large language models, natural language processing, or generative artificial intelligence solutions.
- 2\+ years of experience using containerization and orchestration tools such as Docker or Kubernetes.
- 1\+ years of experience supporting model monitoring, model governance, or machine learning operations processes.
The wage range for this role takes into account a wide range of factors 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 $159,100 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 $159K-$265K range is above the 75th percentile for Data Engineer roles in our dataset (median: $168K across 38 roles with salary data).
Role Details
About This Role
Data Engineers build the pipelines that feed AI models. They design ETL workflows, manage data lakes, and ensure training and inference data is clean, timely, and accessible. Without good data engineering, AI projects fail. It's that simple.
The AI era has expanded the data engineer's scope far beyond batch ETL jobs. You're building real-time embedding pipelines for RAG systems, managing vector databases, ensuring training data quality at scale, and building the infrastructure that lets ML teams iterate on data as fast as they iterate on models. Data quality is the biggest predictor of model quality, and you're the person responsible for it.
Across the 4,133 AI roles we're tracking, Data Engineer positions make up 1% of the market. At Deloitte, this role fits into their broader AI and engineering organization.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
What the Work Looks Like
A typical week includes: debugging a data pipeline that's producing stale embeddings for the RAG system, optimizing a Spark job that processes training data, building a data quality monitoring dashboard, meeting with the ML team to understand their next data requirements, and writing dbt models that transform raw event data into ML-ready features. The work is deeply technical and high-impact.
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
Skills Required
SQL, Python, and distributed systems (Spark, Airflow, dbt) are core. Cloud data platforms (Snowflake, BigQuery, Redshift) are increasingly standard. Many AI-focused roles also want familiarity with vector databases and embedding pipelines. Understanding data modeling, pipeline orchestration, and data quality frameworks covers the essentials.
AI-specific data engineering skills include: building feature stores, managing training data versioning, implementing data lineage tracking, and building real-time embedding pipelines. Experience with streaming systems (Kafka, Flink) is valuable for real-time AI applications. Understanding ML data requirements (balanced datasets, data augmentation, evaluation set construction) makes you much more effective working with ML teams.
Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
Compensation Benchmarks
Data Engineer roles pay a median of $208,300 based on 273 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $159K 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 Data Engineer roles include Backend Engineer, Database Administrator, Analytics Engineer.
From here, career progression typically leads toward Senior Data Engineer, ML Engineer, Data Platform Lead.
Master SQL and Python first. Then learn a distributed processing framework (Spark or its modern alternatives) and a pipeline orchestrator (Airflow, Dagster, Prefect). Build a portfolio project that demonstrates end-to-end pipeline construction: ingest, transform, validate, serve. If you want to specialize in AI data engineering, add vector databases and embedding pipelines to your skill set.
What to Expect in Interviews
Expect SQL deep-dives (query optimization, partitioning strategies, data modeling), Python coding focused on data pipeline patterns, and system design questions about building scalable ETL workflows. Companies with ML teams will ask about feature stores, embedding pipelines, and training data management. Be ready to discuss data quality monitoring, pipeline orchestration, and how you'd handle schema evolution in a production data lake.
When evaluating opportunities: Strong postings specify the data stack, mention ML pipeline work, and describe the scale of data you'll be working with. Look for companies that understand the connection between data quality and model quality. Avoid roles that conflate data engineering with data analysis.
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).
Data Engineer demand in AI contexts is strong and growing. Every company building AI needs clean, reliable data pipelines. The shift toward real-time AI applications (chatbots, recommendation engines, agent systems) means data engineering is more critical than ever. Companies are willing to pay premium salaries for data engineers with AI/ML pipeline experience.
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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