E-3 Visa AI ML Platform Jobs
AI ML Platform roles qualify as E-3 specialty occupations when tied to a relevant degree in computer science, data science, or a related field. Australian professionals can secure E-3 visa sponsorship without competing in the H-1B lottery, with two-year periods renewable indefinitely as long as you hold a qualifying offer.
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Get Access To All JobsINTRODUCTION
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking a Lead ML Platform Engineer (SRE / FTE / Onsite) to join our team in Charlotte, North Carolina (US-NC), United States (US).
Job Duties and Responsibilities:
The Lead ML Platform Engineer provides architecture and hands-on engineering leadership for the Cortex Predictive AI Platform across cloud and on-premises environments. This role will establish and implement reusable, secure, scalable standards that enable data scientists, ML engineers, and application teams to build, validate, deploy, monitor, and operate predictive models efficiently and reliably.
The successful candidate will lead technical design and engineering decisions across the ML platform lifecycle, including governed data and feature access, model development environments, training and validation workflows, model delivery pipelines, real-time and batch inference, observability, reliability, and operational readiness. This role will also mentor engineering teams and transfer knowledge to support sustainable platform operations and adoption.
Key Responsibilities
- Define and lead the target architecture for predictive AI and ML platform capabilities spanning public cloud and on-premises environments.
- Design, build, and operate reusable platform services supporting the end-to-end ML lifecycle: governed data and features, model development, training, validation, deployment, inference, monitoring, and operations.
- Establish scalable reference architectures, engineering standards, reusable templates, and implementation patterns for ML workloads across the Cortex portfolio.
- Lead platform engineering for GCP and multi-cloud environments, including secure connectivity, identity, network controls, compute, storage, and managed AI/ML services where applicable.
- Design and operate Kubernetes-based ML platforms using GKE, OpenShift, and associated container, workload orchestration, and resource-management capabilities.
- Implement and improve MLOps capabilities for experiment tracking, model packaging, validation, approval gates, model registry integration, deployment automation, rollback, and lifecycle management.
- Build CI/CD pipelines and infrastructure automation for platform services, ML workflows, model delivery, and environment provisioning.
- Enable model migration from legacy environments into standardized Cortex platform patterns, minimizing delivery risk and operational disruption.
- Engineer production-grade real-time and batch inference capabilities, including API-based serving, scalable runtime patterns, resiliency, performance, and operational support.
- Partner with data engineering, data governance, security, privacy, risk, model validation, and application teams to ensure data protection and control requirements are embedded into platform design.
- Implement platform observability, including logs, metrics, traces, dashboards, alerts, service-level indicators, service-level objectives, and operational runbooks.
- Drive reliability engineering practices for ML platform services, including capacity planning, high availability, disaster recovery, incident management, root-cause analysis, and continuous improvement.
- Ensure platform designs meet enterprise security requirements for authentication, authorization, secrets management, encryption, data access, auditability, and environment isolation.
- Provide technical leadership, architecture reviews, code reviews, design guidance, and mentoring to ML platform engineers and adjacent delivery teams.
- Produce clear technical documentation, reference implementations, operational procedures, and knowledge-transfer materials to enable self-service adoption and long-term support.
BASIC QUALIFICATIONS
- 8+ years of experience in platform engineering, cloud engineering, infrastructure engineering, SRE, MLOps, or related technical roles.
- 4+ years of experience designing, building, or operating enterprise AI/ML or data platforms.
- Demonstrated experience leading architecture and engineering delivery for complex, production-grade cloud and/or on-premises platforms.
- Strong hands-on experience with GCP and working knowledge of multi-cloud or hybrid-cloud architecture.
- Experience with Kubernetes-based platforms, including GKE and OpenShift, in production environments.
- Strong experience implementing MLOps capabilities, model lifecycle workflows, or ML platform services.
- Proficiency in Python for platform automation, integration, operational tooling, or ML workflow development.
- Experience with CI/CD, Git-based development, automated testing, deployment automation, and infrastructure-as-code practices.
- Strong understanding of enterprise security, data protection, identity and access management, secrets management, encryption, audit logging, and secure software delivery.
- Experience implementing observability, monitoring, alerting, dashboards, SLOs, incident response, and operational runbooks.
- Experience mentoring engineers and communicating technical architecture decisions to engineering, product, security, data, and executive stakeholders.
REQUIRED SKILLS / KNOWLEDGE
- Enterprise ML platform architecture and end-to-end predictive model lifecycle management.
- GCP, hybrid cloud, multi-cloud, on-premises platform, networking, identity, and security concepts.
- Kubernetes, GKE, OpenShift, containers, workload orchestration, and scalable compute platforms.
- MLOps, model development environments, model registries, validation workflows, model deployment, and model monitoring.
- Python, CI/CD, Git, automated testing, infrastructure automation, and API-based integration.
- Real-time and batch inference architecture, model-serving patterns, performance optimization, and operational support.
- Data protection, governance, access controls, encryption, auditability, and regulated-platform design.
- Observability, telemetry, dashboards, alerting, SLI/SLO design, reliability engineering, and production troubleshooting.
- Technical leadership, reusable pattern development, engineering documentation, and knowledge transfer.
PREFERRED QUALIFICATIONS
- Experience with Vertex AI or comparable cloud ML platform services.
- Experience designing or operating on-premises AI/ML platforms, private cloud, or hybrid ML workloads.
- Experience with feature stores, model registries, experiment tracking, data lineage, model governance, or model risk-management processes.
- Experience supporting model migration, platform modernization, or transition from legacy data science and ML environments.
- Experience with real-time, low-latency model-serving systems and event-driven inference architectures.
- Experience with Terraform, Helm, Argo CD, Jenkins, GitHub Actions, GitLab CI, or similar automation and deployment tooling.
- Experience in banking, financial services, healthcare, insurance, or another regulated enterprise environment.
- Experience establishing self-service platform capabilities for data scientists, ML engineers, and application teams.
EXPECTED OUTCOMES
- A secure, scalable, and reusable Cortex ML platform architecture spanning public cloud and on-premises environments.
- Standardized MLOps, CI/CD, and model-delivery patterns that reduce time to train, validate, deploy, and operate predictive models.
- Reliable platform capabilities for governed data and features, model migration, batch and real-time inference, and production operations.
- Improved observability, resiliency, service-level management, and operational readiness for ML platform services and models.
- Reusable engineering standards, reference implementations, documentation, and knowledge-transfer assets that enable self-service adoption and sustainable platform support.
COMPENSATION
- The starting pay range for this role is $83,520.00 - $125,280.00. Actual compensation will depend on a number of factors, including the candidate’s relevant experience, technical skills, and other qualifications.
This position may also be eligible for incentive compensation based on individual and/or company performance.
This position is eligible for company benefits including medical, dental, and vision insurance with an employer contribution, flexible spending or health savings account, life and ADD insurance, short and long term disability coverage, paid time off, employee assistance, participation in a 401k program with company match, and additional voluntary or legally-required benefits.
ABOUT NTT DATA
NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. Our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.
Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form.
NTT DATA endeavors to make its website accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here. If you'd like more information on your EEO rights under the law, please click here. For Pay Transparency information, please click here.
INTRODUCTION
NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now.
We are currently seeking a Lead ML Platform Engineer (SRE / FTE / Onsite) to join our team in Charlotte, North Carolina (US-NC), United States (US).
Job Duties and Responsibilities:
The Lead ML Platform Engineer provides architecture and hands-on engineering leadership for the Cortex Predictive AI Platform across cloud and on-premises environments. This role will establish and implement reusable, secure, scalable standards that enable data scientists, ML engineers, and application teams to build, validate, deploy, monitor, and operate predictive models efficiently and reliably.
The successful candidate will lead technical design and engineering decisions across the ML platform lifecycle, including governed data and feature access, model development environments, training and validation workflows, model delivery pipelines, real-time and batch inference, observability, reliability, and operational readiness. This role will also mentor engineering teams and transfer knowledge to support sustainable platform operations and adoption.
Key Responsibilities
- Define and lead the target architecture for predictive AI and ML platform capabilities spanning public cloud and on-premises environments.
- Design, build, and operate reusable platform services supporting the end-to-end ML lifecycle: governed data and features, model development, training, validation, deployment, inference, monitoring, and operations.
- Establish scalable reference architectures, engineering standards, reusable templates, and implementation patterns for ML workloads across the Cortex portfolio.
- Lead platform engineering for GCP and multi-cloud environments, including secure connectivity, identity, network controls, compute, storage, and managed AI/ML services where applicable.
- Design and operate Kubernetes-based ML platforms using GKE, OpenShift, and associated container, workload orchestration, and resource-management capabilities.
- Implement and improve MLOps capabilities for experiment tracking, model packaging, validation, approval gates, model registry integration, deployment automation, rollback, and lifecycle management.
- Build CI/CD pipelines and infrastructure automation for platform services, ML workflows, model delivery, and environment provisioning.
- Enable model migration from legacy environments into standardized Cortex platform patterns, minimizing delivery risk and operational disruption.
- Engineer production-grade real-time and batch inference capabilities, including API-based serving, scalable runtime patterns, resiliency, performance, and operational support.
- Partner with data engineering, data governance, security, privacy, risk, model validation, and application teams to ensure data protection and control requirements are embedded into platform design.
- Implement platform observability, including logs, metrics, traces, dashboards, alerts, service-level indicators, service-level objectives, and operational runbooks.
- Drive reliability engineering practices for ML platform services, including capacity planning, high availability, disaster recovery, incident management, root-cause analysis, and continuous improvement.
- Ensure platform designs meet enterprise security requirements for authentication, authorization, secrets management, encryption, data access, auditability, and environment isolation.
- Provide technical leadership, architecture reviews, code reviews, design guidance, and mentoring to ML platform engineers and adjacent delivery teams.
- Produce clear technical documentation, reference implementations, operational procedures, and knowledge-transfer materials to enable self-service adoption and long-term support.
BASIC QUALIFICATIONS
- 8+ years of experience in platform engineering, cloud engineering, infrastructure engineering, SRE, MLOps, or related technical roles.
- 4+ years of experience designing, building, or operating enterprise AI/ML or data platforms.
- Demonstrated experience leading architecture and engineering delivery for complex, production-grade cloud and/or on-premises platforms.
- Strong hands-on experience with GCP and working knowledge of multi-cloud or hybrid-cloud architecture.
- Experience with Kubernetes-based platforms, including GKE and OpenShift, in production environments.
- Strong experience implementing MLOps capabilities, model lifecycle workflows, or ML platform services.
- Proficiency in Python for platform automation, integration, operational tooling, or ML workflow development.
- Experience with CI/CD, Git-based development, automated testing, deployment automation, and infrastructure-as-code practices.
- Strong understanding of enterprise security, data protection, identity and access management, secrets management, encryption, audit logging, and secure software delivery.
- Experience implementing observability, monitoring, alerting, dashboards, SLOs, incident response, and operational runbooks.
- Experience mentoring engineers and communicating technical architecture decisions to engineering, product, security, data, and executive stakeholders.
REQUIRED SKILLS / KNOWLEDGE
- Enterprise ML platform architecture and end-to-end predictive model lifecycle management.
- GCP, hybrid cloud, multi-cloud, on-premises platform, networking, identity, and security concepts.
- Kubernetes, GKE, OpenShift, containers, workload orchestration, and scalable compute platforms.
- MLOps, model development environments, model registries, validation workflows, model deployment, and model monitoring.
- Python, CI/CD, Git, automated testing, infrastructure automation, and API-based integration.
- Real-time and batch inference architecture, model-serving patterns, performance optimization, and operational support.
- Data protection, governance, access controls, encryption, auditability, and regulated-platform design.
- Observability, telemetry, dashboards, alerting, SLI/SLO design, reliability engineering, and production troubleshooting.
- Technical leadership, reusable pattern development, engineering documentation, and knowledge transfer.
PREFERRED QUALIFICATIONS
- Experience with Vertex AI or comparable cloud ML platform services.
- Experience designing or operating on-premises AI/ML platforms, private cloud, or hybrid ML workloads.
- Experience with feature stores, model registries, experiment tracking, data lineage, model governance, or model risk-management processes.
- Experience supporting model migration, platform modernization, or transition from legacy data science and ML environments.
- Experience with real-time, low-latency model-serving systems and event-driven inference architectures.
- Experience with Terraform, Helm, Argo CD, Jenkins, GitHub Actions, GitLab CI, or similar automation and deployment tooling.
- Experience in banking, financial services, healthcare, insurance, or another regulated enterprise environment.
- Experience establishing self-service platform capabilities for data scientists, ML engineers, and application teams.
EXPECTED OUTCOMES
- A secure, scalable, and reusable Cortex ML platform architecture spanning public cloud and on-premises environments.
- Standardized MLOps, CI/CD, and model-delivery patterns that reduce time to train, validate, deploy, and operate predictive models.
- Reliable platform capabilities for governed data and features, model migration, batch and real-time inference, and production operations.
- Improved observability, resiliency, service-level management, and operational readiness for ML platform services and models.
- Reusable engineering standards, reference implementations, documentation, and knowledge-transfer assets that enable self-service adoption and sustainable platform support.
COMPENSATION
- The starting pay range for this role is $83,520.00 - $125,280.00. Actual compensation will depend on a number of factors, including the candidate’s relevant experience, technical skills, and other qualifications.
This position may also be eligible for incentive compensation based on individual and/or company performance.
This position is eligible for company benefits including medical, dental, and vision insurance with an employer contribution, flexible spending or health savings account, life and ADD insurance, short and long term disability coverage, paid time off, employee assistance, participation in a 401k program with company match, and additional voluntary or legally-required benefits.
ABOUT NTT DATA
NTT DATA is a $30 billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world's leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. Our consulting and Industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 50 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is a part of NTT Group, which invests over $3 billion each year in R&D.
Whenever possible, we hire locally to NTT DATA offices or client sites. This ensures we can provide timely and effective support tailored to each client’s needs. While many positions offer remote or hybrid work options, these arrangements are subject to change based on client requirements. For employees near an NTT DATA office or client site, in-office attendance may be required for meetings or events, depending on business needs. At NTT DATA, we are committed to staying flexible and meeting the evolving needs of both our clients and employees. NTT DATA recruiters will never ask for payment or banking information and will only use @nttdata.com, @nttdatafed.com and @talent.nttdataservices.com email addresses. If you are requested to provide payment or disclose banking information, please submit a contact us form.
NTT DATA endeavors to make its website accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us. This contact information is for accommodation requests only and cannot be used to inquire about the status of applications. NTT DATA is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. For our EEO Policy Statement, please click here. If you'd like more information on your EEO rights under the law, please click here. For Pay Transparency information, please click here.
See all 557+ E-3 Visa AI ML Platform Jobs
Sign up for free to unlock all listings, filter by visa type, and get alerts for new E-3 Visa AI ML Platform Jobs.
Get Access To All JobsTips for Finding E-3 Visa Sponsorship in AI ML Platform
Align your degree to the role
AI ML Platform positions require a degree in computer science, data science, or engineering to satisfy the E-3 visa specialty occupation standard. If your Australian three-year bachelor's is questioned, a credential evaluation confirming U.S. equivalency resolves it before the LCA stage.
Target employers with active LCA histories
Search DOL's OFLC disclosure data for employers who have filed Labor Condition Applications for machine learning or platform engineering roles. Prior LCA filings signal that the employer already understands E-3 sponsorship mechanics, reducing friction during offer negotiation.
Clarify sponsorship scope before signing
Some employers will cover LCA filing but not consulate fees or visa paperwork. Confirm in writing which costs the employer absorbs. The LCA itself is a DOL requirement the employer must file, so that cost is non-negotiable on their end.
Use Migrate Mate's E-3 filing service for end-to-end support
Once you have an offer, use Migrate Mate's E-3 filing service to handle your LCA and visa paperwork from certification through consulate appointment. This avoids coordination errors between your employer's HR team and the consulate's document requirements.
Prepare for specialization questions at the consulate
Consular officers assess whether your role genuinely requires theoretical and practical application of AI and ML at a degree level. Bring documentation showing model development, platform architecture, or research responsibilities, not just operational or support duties.
Time your application around project milestones
E-3 visas are processed at the consulate without a filing queue, so your start date depends on appointment availability, not USCIS adjudication windows. Schedule your interview at least four to six weeks before your intended start date to allow for passport return.
E-3 Visa AI ML Platform: Frequently Asked Questions
How do I find AI ML Platform jobs that offer E-3 visa sponsorship?
Migrate Mate is the most direct way to search for AI ML Platform roles where employers are open to E-3 sponsorship. Most general job boards don't filter by visa type, so you end up screening dozens of listings manually. Migrate Mate surfaces roles matched to E-3 eligibility, letting you focus on applications rather than eligibility research.
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 ML Platform role qualify as an E-3 specialty occupation?
Yes, provided the position requires a bachelor's degree or higher in a directly related field such as computer science, machine learning, or software engineering. The role must call for theoretical and practical application of that knowledge. Generic platform or DevOps titles without an ML-specific degree requirement can fail the specialty occupation test, so your offer letter should specify the degree field the employer requires.
How does the E-3 compare to the H-1B for AI ML Platform roles?
The E-3 has no lottery and no annual cap, so an Australian professional with a qualifying offer can apply year-round without waiting for a selection round. H-1B registrations are capped at 85,000 annually and subject to a randomized lottery, meaning many qualified candidates are simply not selected. For AI ML Platform roles where demand is consistent, the E-3 removes the single biggest uncertainty in the U.S. work visa process.
Can I switch AI ML Platform employers while on an E-3 visa?
Yes, but you need a new LCA certified by DOL and a new E-3 visa stamp before starting with the new employer. Unlike some visa categories, E-3 portability is not automatic. If you're already in the U.S., you can continue working for your current employer while the new LCA is processed, but you'll need a fresh consulate appointment once you travel internationally to get the new stamp.