E-3 Visa AI Data Engineer Jobs
AI Data Engineer roles qualify for E-3 visa sponsorship as specialty occupations requiring a bachelor's degree in computer science, data engineering, or a related field. The E-3 has no lottery and no annual cap, so Australian professionals can apply as soon as they have a qualifying job offer from a U.S. employer.
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Get Access To All JobsAI Data Engineer - Manager
Our Human Capital practice is at the forefront of transforming the nature of work. As converging forces reshape industries, our team uniquely addresses the complexities of work, workforce, and workplace dynamics. We leverage sector-specific insights and cross-domain perspectives to help organizations tackle their most challenging workforce issues and align talent strategies with their strategic visions. Our practice is renowned for making work better for humans and humans better at work. Be part of this exciting era of change and join us on this transformative journey.
Recruiting for this role ends on August 30, 2026
Work You'll Do:
The AI Data Engineer will lead the data architecture and engineering delivery that enables AI/ML/GenAI solutions, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption. You will design and operationalize modern data and retrieval foundations to support LLM-powered applications (e.g., Claude, GPT/Codex, Gemini) including patterns such as RAG, embeddings, vector search, and governed access to structured and unstructured data. You will manage day-to-day delivery with an onshore/offshore team, partnering with data science, ML engineering, and product stakeholders to translate use cases into production-ready pipelines and platforms with strong data governance, lineage, quality controls, and monitoring. This role blends hands-on technical leadership with delivery management and team development, driving consistent engineering standards and measurable outcomes in client environments.
Strategic Alignment and Vision
Help define the AI/ML/GenAI technical direction and vision, ensuring alignment with strategic goals and digital transformation efforts.
Translate the vision of business leaders into realistic technical implementations, while identifying misaligned initiatives and impractical use cases
Architectural Design
* Design end-to-end AI architectures, from data ingestion to model deployment, integrating with cloud and on-premises systems.
Design and Technology Selection
Select appropriate technologies from a pool of open-source and commercial offerings, considering deployment models and integration with existing tools.
Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models.
Research and Development
Conduct research to provide technical solutions to scale AI/ML powered features for real-world challenges, making trade-offs based on quality, scalability, performance, and cost.
Lead the development of AI models (e.g., machine learning, natural language processing, computer vision) and implement scalable AI solutions.
Collaboration and Stakeholder Engagement
Collaborate with Enterprise, Application, Data & DevOps teams, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot use cases and discuss best design.
Gather inputs from multiple stakeholders to align technical implementation with existing and future requirements.
Consulting & Advisory
* Serve as a technical advisor to leadership, providing insights on AI trends, potential business impacts, and implementation best practices.
Operational Excellence and Continuous Improvement
Be responsible for the successful execution of AI-powered applications using agile methodology.
Audit AI tools and practices across data, models and software engineering, focusing on continuous improvement and feedback mechanisms.
* Contribute to standardizing CI/CD pipelines, user and service roles, and container creation, model consumption, testing, and deployment methodology based on business and security requirements.
Risk Management and Ethical Considerations
Work closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations.
Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
Product Strategy and Business Understanding
Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
Break down client problems and bring an understanding of leading technology, analytics methods, tools, and operating model approaches.
Tool Development and Data Management
Build tools and capabilities that assist with data ingestion, feature engineering, data management, and organization.
Design, implement, and maintain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of machine learning infrastructure.
The Team
Our Insights, Innovation & Operate Offering is designed to enhance key aspects of our clients' businesses by leveraging cutting-edge technology, data, and a blend of deep technical and human expertise. We innovate and deliver creative, industry-specific solutions that streamline operations and accelerate speed-to-value.
Required Qualifications:
Bachelor's degree in Computer Science, Statistics, Data Science, Information Systems or related field.
6+ years of consulting experience leading delivery teams, including onshore and offshore team members
6+ years of experience gathering non-functional requirements and defining application architecture frameworks, including validation and testing deliverables
5+ years of experience working in an AI environment
5+ years of experience translating requirements into client ready design documents
5+ years of experience in software application architecture analysis, design, and delivery
5+ years of experience executing full system development life cycle implementations
Ability to travel 0-25%, on average, based on the work you do and the clients and industries/sectors you serve.
* Limited immigration sponsorship may be available.
Preferred Qualifications:
Advanced degrees such as Masters or PhD are preferred
Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect
5+ years of experience in Data Science, Statistics, and Machine Learning
5+ years of experience in Generative AI/LLMs, preferably experienced in delivering and productionizing
5+ years of experience in machine learning model development, natural language processing, and data analysis; Experienced in Supervised and Unsupervised learning, feature engineering, model training, and deployment
5+ year of experience in implementing cloud-based AI/ML workloads on any of AWS, Microsoft and Azure.
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 $130,800-241,000.
Possible Locations:
Atlanta, Austin, Baltimore, Boston, Charlotte, Chicago, Cincinnati, Cleveland, Columbus, Costa Mesa, Dallas, Denver, Detroit, Hartford, Houston, Indianapolis, Jacksonville, Kansas City, Las Vegas, Los Angeles, McLean, Miami, Milwaukee, Nashville, New Orleans, New York, Philadelphia, Pittsburgh, Portland, Raleigh, Richmond, Sacramento, San Antonio, San Diego, San Francisco, San Jose, Seattle, St. Louis, Stamford, Tampa, Tempe
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.
Information for applicants with a need for accommodation: https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-assistance-for-disabled-applicants.html
For more information about Human Capital, visit our landing page at: https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-human-capital-consulting-jobs.html
HCFY26 #IIOFY26
AI Data Engineer - Manager
Our Human Capital practice is at the forefront of transforming the nature of work. As converging forces reshape industries, our team uniquely addresses the complexities of work, workforce, and workplace dynamics. We leverage sector-specific insights and cross-domain perspectives to help organizations tackle their most challenging workforce issues and align talent strategies with their strategic visions. Our practice is renowned for making work better for humans and humans better at work. Be part of this exciting era of change and join us on this transformative journey.
Recruiting for this role ends on August 30, 2026
Work You'll Do:
The AI Data Engineer will lead the data architecture and engineering delivery that enables AI/ML/GenAI solutions, ensuring data is trusted, secure, observable, and scalable from ingestion through consumption. You will design and operationalize modern data and retrieval foundations to support LLM-powered applications (e.g., Claude, GPT/Codex, Gemini) including patterns such as RAG, embeddings, vector search, and governed access to structured and unstructured data. You will manage day-to-day delivery with an onshore/offshore team, partnering with data science, ML engineering, and product stakeholders to translate use cases into production-ready pipelines and platforms with strong data governance, lineage, quality controls, and monitoring. This role blends hands-on technical leadership with delivery management and team development, driving consistent engineering standards and measurable outcomes in client environments.
Strategic Alignment and Vision
Help define the AI/ML/GenAI technical direction and vision, ensuring alignment with strategic goals and digital transformation efforts.
Translate the vision of business leaders into realistic technical implementations, while identifying misaligned initiatives and impractical use cases
Architectural Design
* Design end-to-end AI architectures, from data ingestion to model deployment, integrating with cloud and on-premises systems.
Design and Technology Selection
Select appropriate technologies from a pool of open-source and commercial offerings, considering deployment models and integration with existing tools.
Understand and contribute to MLOps and LLMOps, focusing on operational capabilities and infrastructure to deploy and manage machine learning models and large language models.
Research and Development
Conduct research to provide technical solutions to scale AI/ML powered features for real-world challenges, making trade-offs based on quality, scalability, performance, and cost.
Lead the development of AI models (e.g., machine learning, natural language processing, computer vision) and implement scalable AI solutions.
Collaboration and Stakeholder Engagement
Collaborate with Enterprise, Application, Data & DevOps teams, Data scientists, Machine Learning & GenAI Engineers, and Business teams to pilot use cases and discuss best design.
Gather inputs from multiple stakeholders to align technical implementation with existing and future requirements.
Consulting & Advisory
* Serve as a technical advisor to leadership, providing insights on AI trends, potential business impacts, and implementation best practices.
Operational Excellence and Continuous Improvement
Be responsible for the successful execution of AI-powered applications using agile methodology.
Audit AI tools and practices across data, models and software engineering, focusing on continuous improvement and feedback mechanisms.
* Contribute to standardizing CI/CD pipelines, user and service roles, and container creation, model consumption, testing, and deployment methodology based on business and security requirements.
Risk Management and Ethical Considerations
Work closely with security and risk leaders to foresee and mitigate risks, ensuring ethical AI implementation and compliance with upcoming regulations.
Address potential issues such as training data poisoning, AI model theft, and adversarial samples.
Product Strategy and Business Understanding
Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
Break down client problems and bring an understanding of leading technology, analytics methods, tools, and operating model approaches.
Tool Development and Data Management
Build tools and capabilities that assist with data ingestion, feature engineering, data management, and organization.
Design, implement, and maintain distributed computing solutions for data processing and model training, ensuring the security, scalability, and reliability of machine learning infrastructure.
The Team
Our Insights, Innovation & Operate Offering is designed to enhance key aspects of our clients' businesses by leveraging cutting-edge technology, data, and a blend of deep technical and human expertise. We innovate and deliver creative, industry-specific solutions that streamline operations and accelerate speed-to-value.
Required Qualifications:
Bachelor's degree in Computer Science, Statistics, Data Science, Information Systems or related field.
6+ years of consulting experience leading delivery teams, including onshore and offshore team members
6+ years of experience gathering non-functional requirements and defining application architecture frameworks, including validation and testing deliverables
5+ years of experience working in an AI environment
5+ years of experience translating requirements into client ready design documents
5+ years of experience in software application architecture analysis, design, and delivery
5+ years of experience executing full system development life cycle implementations
Ability to travel 0-25%, on average, based on the work you do and the clients and industries/sectors you serve.
* Limited immigration sponsorship may be available.
Preferred Qualifications:
Advanced degrees such as Masters or PhD are preferred
Certifications in AI/ML technologies and Cloud platforms, such as AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer, Azure AI Engineer, Azure Data Scientist, or Azure Solutions Architect
5+ years of experience in Data Science, Statistics, and Machine Learning
5+ years of experience in Generative AI/LLMs, preferably experienced in delivering and productionizing
5+ years of experience in machine learning model development, natural language processing, and data analysis; Experienced in Supervised and Unsupervised learning, feature engineering, model training, and deployment
5+ year of experience in implementing cloud-based AI/ML workloads on any of AWS, Microsoft and Azure.
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 $130,800-241,000.
Possible Locations:
Atlanta, Austin, Baltimore, Boston, Charlotte, Chicago, Cincinnati, Cleveland, Columbus, Costa Mesa, Dallas, Denver, Detroit, Hartford, Houston, Indianapolis, Jacksonville, Kansas City, Las Vegas, Los Angeles, McLean, Miami, Milwaukee, Nashville, New Orleans, New York, Philadelphia, Pittsburgh, Portland, Raleigh, Richmond, Sacramento, San Antonio, San Diego, San Francisco, San Jose, Seattle, St. Louis, Stamford, Tampa, Tempe
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.
Information for applicants with a need for accommodation: https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-assistance-for-disabled-applicants.html
For more information about Human Capital, visit our landing page at: https://www2.deloitte.com/us/en/pages/careers/articles/join-deloitte-human-capital-consulting-jobs.html
HCFY26 #IIOFY26
See all 2,476+ E-3 Visa AI Data Engineer Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new E-3 Visa AI Data Engineer Jobs.
Get Access To All JobsTips for Finding E-3 Visa Sponsorship as an AI Data Engineer
Align your credentials to U.S. specialty occupation standards
DOL requires your degree field to directly relate to the AI Data Engineer role. A computer science or data engineering degree maps cleanly, but if yours is in a tangential field, document how your coursework covers machine learning pipelines, distributed systems, or cloud data architecture.
Target employers with active LCA filing histories
U.S. employers file a Labor Condition Application with DOL before sponsoring any E-3 visa. Search DOL's Office of Foreign Labor Certification disclosure data to confirm a prospective employer has filed LCAs for data engineering or AI roles before approaching them.
Search for E-3 sponsorship roles on Migrate Mate
Most job boards don't filter by visa type, so you end up cold-applying to roles where sponsorship was never on the table. Migrate Mate surfaces AI Data Engineer roles specifically listed with E-3 sponsorship, cutting out the guesswork before you invest time in applications.
Negotiate LCA filing into your offer timeline
The LCA must be certified by DOL before your visa application, which adds roughly one to two weeks to the timeline. Raise this during offer negotiation so your start date accounts for LCA certification and consulate appointment scheduling, not just your notice period.
Use Migrate Mate's E-3 filing service to coordinate LCA and consulate prep
E-3 applications involve the employer filing the LCA and you filing the DS-160 and attending a consulate interview. Migrate Mate's E-3 filing service manages the entire process from offer to consulate appointment, reducing coordination errors that can delay your start date.
Prepare for specialty occupation questions at your consulate interview
Consular officers may probe whether your AI Data Engineer role genuinely requires a bachelor's degree or higher. Bring documentation showing the technical depth of the role, such as your job description referencing specific ML frameworks, data pipeline tools, or cloud platforms, alongside your degree transcripts.
E-3 Visa AI Data Engineer: Frequently Asked Questions
How do I find AI Data Engineer jobs that offer E-3 visa sponsorship?
Most job listings don't specify visa sponsorship type, making it hard to know whether an employer will support an E-3 before you apply. Migrate Mate filters roles by E-3 sponsorship availability, so you can search AI Data Engineer positions where employers have already indicated willingness to sponsor, saving you from dead-end applications.
How much does it cost to get an E-3 visa?
Migrate Mate's E-3 filing service covers the entire process for $499, including the Labor Condition Application, visa document preparation, and consulate appointment guidance. Traditional immigration lawyers charge $2,000–$5,000+ for the same work. The E-3 has less paperwork than most work visas, so paying thousands for legal help is usually unnecessary.
Does an AI Data Engineer role qualify as a specialty occupation for the E-3?
Yes, AI Data Engineer roles consistently qualify because they require theoretical and practical application of highly specialized knowledge, typically a bachelor's degree or higher in computer science, data engineering, or a closely related field. The DOL and USCIS assess specialty occupation on a case-by-case basis, so your job offer letter should specify the degree requirement and the technical complexity of the role.
How does the E-3 compare to the H-1B for AI Data Engineers?
For Australian nationals, the E-3 is significantly more practical than the H-1B visa. The H-1B has an annual cap of 85,000 slots and a randomized lottery, meaning many qualified engineers wait years or never get selected. The E-3 has a separate 10,500 annual allocation that has never been fully used, so there's no lottery and no waiting period once you have a qualifying job offer.
Can I switch employers after starting work on an E-3 as an AI Data Engineer?
Yes, but each new employer must file a fresh LCA with DOL and you'll need a new E-3 visa tied to that employer. You can begin work with the new employer once their LCA is certified and you've obtained updated E-3 documentation. There's no portability provision like the H-1B, so don't resign before the new employer's paperwork is in order.