Machine Learning Visa Sponsorship Jobs in North Carolina
North Carolina's machine learning job market is anchored in the Research Triangle, where companies like IBM, Red Hat, and Lenovo maintain significant AI and data science operations. Duke University and NC State feed a steady pipeline of ML talent into Raleigh, Durham, and Charlotte. Many of these employers have established visa sponsorship programs for qualified international candidates.
Find Machine Learning JobsOverview
Showing 5 of 31+ Machine Learning Jobs in North Carolina with Visa Sponsorship
See all 31+ Machine Learning Jobs in North Carolina with Visa Sponsorship
Sign up for free to unlock all listings, filter by visa type, and get alerts for new Machine Learning Jobs in North Carolina with Visa Sponsorship.
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.
Machine Learning Job Roles in North Carolina
See all 31+ Machine Learning Jobs in North Carolina
Sign up for free to filter by visa type, set job alerts, and find employers with verified sponsorship history.
Search Machine Learning Jobs in North CarolinaMachine Learning Jobs in North Carolina: Frequently Asked Questions
Which companies sponsor visas for machine learning roles in North Carolina?
IBM, Lenovo, Red Hat, and SAS Institute are among the most active sponsors for machine learning roles in North Carolina, particularly in the Research Triangle area. Financial firms in Charlotte, such as Bank of America and Wells Fargo, also sponsor ML engineers and data scientists through the H-1B visa program. Biotech and life sciences companies in the Durham-Chapel Hill corridor, including those connected to the UNC Health system, have similarly filed H-1B petitions for ML-adjacent roles.
Which visa types are most common for machine learning jobs in North Carolina?
The H-1B is the most common visa for machine learning roles in North Carolina, as ML engineer and data scientist positions typically qualify as specialty occupations requiring at least a bachelor's degree in a related field like computer science or statistics. Candidates already in the U.S. on F-1 OPT, including the 24-month STEM extension, frequently work in ML roles before transitioning to H-1B status. The O-1A is an alternative for candidates with demonstrated exceptional ability or achievement in the field.
Which cities in North Carolina have the most machine learning sponsorship jobs?
Raleigh and Durham, together forming the core of the Research Triangle, concentrate the largest share of machine learning sponsorship activity in the state. The proximity to NC State, Duke, and UNC-Chapel Hill creates a dense employer ecosystem that actively hires international ML talent. Charlotte is the secondary hub, driven by its financial services sector. Smaller but growing concentrations exist in Cary, where SAS Institute is headquartered, and in Chapel Hill.
How to find machine learning visa sponsorship jobs in North Carolina?
Migrate Mate is built specifically for international candidates searching for visa-sponsored roles, including machine learning positions in North Carolina. You can filter directly by state and role type to see current openings from employers with active sponsorship programs. This is particularly useful for narrowing down Research Triangle employers who have filed H-1B petitions for ML engineers and data scientists, saving significant time compared to manually screening general job listings.
Are there any state-specific factors that affect machine learning visa sponsorship in North Carolina?
North Carolina's strong university pipeline, particularly from NC State's computer science and statistics programs and Duke's AI research centers, means employers in the state are experienced with international graduate hiring and OPT-to-H-1B transitions. The Research Triangle Park is home to several large multinationals with established immigration legal teams, which generally makes the sponsorship process more structured. Life sciences and healthcare AI companies in the triangle also benefit from strong NIH and research grant funding, which can support ML hiring.
What is the prevailing wage for sponsored machine learning jobs in North Carolina?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.