McAfee Green Card Visa Sponsorship Jobs USA
McAfee sponsors Green Card visas for technology and cybersecurity professionals, focusing on roles in software engineering, data science, and security research. The company has an established sponsorship track record, making it a credible option for skilled workers pursuing permanent residence in the U.S. through employment-based categories like EB-2 and EB-3.
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Senior AI Platform Engineer - Frisco
Role Overview:
At McAfee, you’ll create solutions in a fun, challenging environment where innovation is encouraged—and excellence is recognized. You’ll use your awesome skills to help engineering.
This role is responsible for designing, building, and scaling enterprise-grade Generative AI platforms and developer ecosystems. The focus is on enabling secure, scalable, reliable, and production-ready GenAI capabilities across the organization leveraging LLMs, AI gateways, Kubernetes, and cloud-native infrastructure.
The role combines deep expertise in platform engineering, AI infrastructure, and generative AI at enterprise scale. It operates with a platform-as-a-product mindset, enabling self-service AI capabilities through developer portals (e.g., Backstage templates and plugins) to accelerate adoption and standardization.
The engineer will partner closely with Security and Governance teams to embed responsible AI practices, enforce policy-driven controls, and provide token-level usage and cost visibility. This role also drives consistency in model access patterns, observability, and lifecycle management of AI services across environments.
This is a Hybrid Position located in Frisco, TX. We are only considering candidates within a commutable distance to the Frisco office. You will be required to be onsite on an as-needed basis; when not working onsite, you will work from your home office. We are only considering candidates within a commutable distance to the office location and are not offering relocation assistance at this time.
About The Role:
Design, build, and scale enterprise-grade Generative AI platforms supporting LLM applications, AI agents, RAG architectures, and multi-model routing.
- Architect and implement secure, scalable AI infrastructure leveraging cloud-native technologies (AWS, GCP, Kubernetes, GKE/EKS).
- Enable self-service AI capabilities for engineering teams through standardized platform services, APIs, and Backstage templates/plugins.
- Build and operate Retrieval-Augmented Generation (RAG) infrastructure, including embedding pipelines and vector stores (OpenSearch, Aurora pgvector).
- Develop and manage enterprise AI gateway capabilities, including model routing, rate limiting, token tracking, and policy enforcement.
- Integrate GenAI services into CI/CD pipelines and platform workflows to enable seamless deployment and lifecycle management.
- Build observability platforms for GenAI systems, tracking token usage, latency, response quality, failure rates, throughput, and cost visibility.
- Own lifecycle management of Kubernetes-based AI platforms including upgrades, patching, scaling.
- Define SLIs/SLOs and reliability benchmarks for AI platform services.
- Implement AI security guardrails including PII redaction, prompt injection defenses, and policy-driven controls.
- Integrate DevSecOps and AI security scanning into deployment pipelines to enforce secure-by-design practices.
- Design AI release validation, risk analysis, and governance frameworks for production readiness.
- Build reusable infrastructure modules and platform automation frameworks using Infrastructure as Code (Terraform or equivalent).
- Develop upgrade and patching strategies for AI platforms with minimal downtime and operational risk.
- Ensure platform security posture, compliance, and lifecycle governance across environments.
- Drive multi-cloud AI platform strategy and lead modernization initiatives across AWS and GCP.
- Partner with Security and Governance teams to enforce responsible AI practices and enterprise standards.
- Drive measurable improvements in developer productivity, platform adoption, and AI cost efficiency through standardized platform capabilities.
About You:
- 10+ years of experience in platform engineering, with hands-on AI/ML or GenAI platform experience.
- Hands-on experience with at least one LLM ecosystem (AWS Bedrock, OpenAI, Anthropic).
- Strong Kubernetes experience (EKS/GKE), including GPU scheduling, autoscaling, and multi-tenant isolation.
- Strong programming expertise in Python and Go; experience building services using FastAPI and gRPC.
- Deep expertise in AWS (IAM, VPC, KMS) and Infrastructure as Code (Terraform).
- Experience building and integrating platforms using Backstage (plugins, templates, self-service patterns).
- Strong understanding of distributed systems and event streaming (Apache Kafka).
- Expertise in CI/CD automation and platform engineering best practices.
- Experience with multi-model orchestration frameworks (LangChain, LlamaIndex).
- Exposure to LLMOps / MLOps tooling for model lifecycle management, evaluation, and versioning.
- Experience building or integrating AI agent frameworks and orchestration patterns.
- Familiarity with AI cost optimization strategies (token efficiency, caching, adaptive routing).
- Experience with prompt engineering frameworks, guardrails, and evaluation techniques.
- Exposure to AI model evaluation frameworks (quality scoring, hallucination detection, benchmarking).
- Experience with vector databases beyond OpenSearch (e.g., Pinecone, Weaviate).
- Familiarity with event-driven architectures for AI workflows (Kafka-based streaming pipelines).
- Experience exposing platform capabilities as reusable APIs, SDKs, templates, and developer tooling.
- Strong understanding of cloud-native architectures and microservices design patterns.
- Experience implementing AI security controls, governance frameworks, and risk mitigation.
- Experience with enterprise AI gateway patterns for model access and control.
- Exposure to agentic AI concepts (MCP, A2A, AI agents) and emerging GenAI orchestration patterns.
- Proven ability to lead architecture reviews, drive platform governance, and influence engineering standards.
- Demonstrated experience driving large-scale engineering transformation initiatives.
- AI/ML certifications such as AWS Machine Learning Specialty, Google Cloud ML Engineer is a plus.
- Cloud architecture certifications (AWS/GCP Solutions Architect) is a plus.
- Kubernetes certifications (CKA, CKAD, CKS) is a plus.
LI-Hybrid
Company Overview
McAfee is a leader in personal security for consumers. Focused on protecting people, not just devices, McAfee consumer solutions adapt to users’ needs in an always online world, empowering them to live securely through integrated, intuitive solutions that protects their families and communities with the right security at the right moment.
Company Benefits and Perks:
We work hard to embrace diversity and inclusion and encourage everyone at McAfee to bring their authentic selves to work every day. We offer a variety of social programs, flexible work hours and family-friendly benefits to all of our employees.
- Bonus Program
- 401k Retirement Plan
- Medical, Dental, Vision, Basic Life, Short Term Disability and Long-Term Disability Coverage
- Paid Parental Leave
- Support for Community Involvement
- 14 Paid Company Holidays
- Unlimited Paid Time Off for Exempt Employees
- 96 Hours of Sick Time and 120 Hours of Vacation for Non-Exempt Employees Accrued Each Year
We're serious about our commitment to diversity which is why McAfee prohibits discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status.
The starting pay range for this position is $107,430.00-$176,490.00. McAfee takes into consideration an individual’s skillset, experience and location in making final salary determinations. For further details, please discuss with the Talent Acquisition Partner.
Job Title:
Senior AI Platform Engineer - Frisco
Role Overview:
At McAfee, you’ll create solutions in a fun, challenging environment where innovation is encouraged—and excellence is recognized. You’ll use your awesome skills to help engineering.
This role is responsible for designing, building, and scaling enterprise-grade Generative AI platforms and developer ecosystems. The focus is on enabling secure, scalable, reliable, and production-ready GenAI capabilities across the organization leveraging LLMs, AI gateways, Kubernetes, and cloud-native infrastructure.
The role combines deep expertise in platform engineering, AI infrastructure, and generative AI at enterprise scale. It operates with a platform-as-a-product mindset, enabling self-service AI capabilities through developer portals (e.g., Backstage templates and plugins) to accelerate adoption and standardization.
The engineer will partner closely with Security and Governance teams to embed responsible AI practices, enforce policy-driven controls, and provide token-level usage and cost visibility. This role also drives consistency in model access patterns, observability, and lifecycle management of AI services across environments.
This is a Hybrid Position located in Frisco, TX. We are only considering candidates within a commutable distance to the Frisco office. You will be required to be onsite on an as-needed basis; when not working onsite, you will work from your home office. We are only considering candidates within a commutable distance to the office location and are not offering relocation assistance at this time.
About The Role:
Design, build, and scale enterprise-grade Generative AI platforms supporting LLM applications, AI agents, RAG architectures, and multi-model routing.
- Architect and implement secure, scalable AI infrastructure leveraging cloud-native technologies (AWS, GCP, Kubernetes, GKE/EKS).
- Enable self-service AI capabilities for engineering teams through standardized platform services, APIs, and Backstage templates/plugins.
- Build and operate Retrieval-Augmented Generation (RAG) infrastructure, including embedding pipelines and vector stores (OpenSearch, Aurora pgvector).
- Develop and manage enterprise AI gateway capabilities, including model routing, rate limiting, token tracking, and policy enforcement.
- Integrate GenAI services into CI/CD pipelines and platform workflows to enable seamless deployment and lifecycle management.
- Build observability platforms for GenAI systems, tracking token usage, latency, response quality, failure rates, throughput, and cost visibility.
- Own lifecycle management of Kubernetes-based AI platforms including upgrades, patching, scaling.
- Define SLIs/SLOs and reliability benchmarks for AI platform services.
- Implement AI security guardrails including PII redaction, prompt injection defenses, and policy-driven controls.
- Integrate DevSecOps and AI security scanning into deployment pipelines to enforce secure-by-design practices.
- Design AI release validation, risk analysis, and governance frameworks for production readiness.
- Build reusable infrastructure modules and platform automation frameworks using Infrastructure as Code (Terraform or equivalent).
- Develop upgrade and patching strategies for AI platforms with minimal downtime and operational risk.
- Ensure platform security posture, compliance, and lifecycle governance across environments.
- Drive multi-cloud AI platform strategy and lead modernization initiatives across AWS and GCP.
- Partner with Security and Governance teams to enforce responsible AI practices and enterprise standards.
- Drive measurable improvements in developer productivity, platform adoption, and AI cost efficiency through standardized platform capabilities.
About You:
- 10+ years of experience in platform engineering, with hands-on AI/ML or GenAI platform experience.
- Hands-on experience with at least one LLM ecosystem (AWS Bedrock, OpenAI, Anthropic).
- Strong Kubernetes experience (EKS/GKE), including GPU scheduling, autoscaling, and multi-tenant isolation.
- Strong programming expertise in Python and Go; experience building services using FastAPI and gRPC.
- Deep expertise in AWS (IAM, VPC, KMS) and Infrastructure as Code (Terraform).
- Experience building and integrating platforms using Backstage (plugins, templates, self-service patterns).
- Strong understanding of distributed systems and event streaming (Apache Kafka).
- Expertise in CI/CD automation and platform engineering best practices.
- Experience with multi-model orchestration frameworks (LangChain, LlamaIndex).
- Exposure to LLMOps / MLOps tooling for model lifecycle management, evaluation, and versioning.
- Experience building or integrating AI agent frameworks and orchestration patterns.
- Familiarity with AI cost optimization strategies (token efficiency, caching, adaptive routing).
- Experience with prompt engineering frameworks, guardrails, and evaluation techniques.
- Exposure to AI model evaluation frameworks (quality scoring, hallucination detection, benchmarking).
- Experience with vector databases beyond OpenSearch (e.g., Pinecone, Weaviate).
- Familiarity with event-driven architectures for AI workflows (Kafka-based streaming pipelines).
- Experience exposing platform capabilities as reusable APIs, SDKs, templates, and developer tooling.
- Strong understanding of cloud-native architectures and microservices design patterns.
- Experience implementing AI security controls, governance frameworks, and risk mitigation.
- Experience with enterprise AI gateway patterns for model access and control.
- Exposure to agentic AI concepts (MCP, A2A, AI agents) and emerging GenAI orchestration patterns.
- Proven ability to lead architecture reviews, drive platform governance, and influence engineering standards.
- Demonstrated experience driving large-scale engineering transformation initiatives.
- AI/ML certifications such as AWS Machine Learning Specialty, Google Cloud ML Engineer is a plus.
- Cloud architecture certifications (AWS/GCP Solutions Architect) is a plus.
- Kubernetes certifications (CKA, CKAD, CKS) is a plus.
LI-Hybrid
Company Overview
McAfee is a leader in personal security for consumers. Focused on protecting people, not just devices, McAfee consumer solutions adapt to users’ needs in an always online world, empowering them to live securely through integrated, intuitive solutions that protects their families and communities with the right security at the right moment.
Company Benefits and Perks:
We work hard to embrace diversity and inclusion and encourage everyone at McAfee to bring their authentic selves to work every day. We offer a variety of social programs, flexible work hours and family-friendly benefits to all of our employees.
- Bonus Program
- 401k Retirement Plan
- Medical, Dental, Vision, Basic Life, Short Term Disability and Long-Term Disability Coverage
- Paid Parental Leave
- Support for Community Involvement
- 14 Paid Company Holidays
- Unlimited Paid Time Off for Exempt Employees
- 96 Hours of Sick Time and 120 Hours of Vacation for Non-Exempt Employees Accrued Each Year
We're serious about our commitment to diversity which is why McAfee prohibits discrimination based on race, color, religion, gender, national origin, age, disability, veteran status, marital status, pregnancy, gender expression or identity, sexual orientation or any other legally protected status.
The starting pay range for this position is $107,430.00-$176,490.00. McAfee takes into consideration an individual’s skillset, experience and location in making final salary determinations. For further details, please discuss with the Talent Acquisition Partner.
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Get Access To All JobsTips for Finding McAfee Green Card Jobs
Target engineering and security research roles
McAfee's Green Card sponsorship activity concentrates in technical roles, software engineers, security researchers, and data scientists. Focusing your applications on these departments gives you the strongest chance of landing a position that qualifies for Green Card sponsorship.
Understand the EB-2 versus EB-3 distinction
McAfee sponsors under both EB-2 and EB-3 categories. Roles requiring advanced degrees or exceptional ability typically fall under EB-2, while positions requiring a bachelor's degree may qualify for EB-3. Knowing which category fits your background helps you frame your application accurately.
Ask about sponsorship commitment during hiring
McAfee's willingness to sponsor varies by team and hiring manager. During interviews, ask directly whether the role supports Green Card sponsorship and at what stage the process typically begins, some teams initiate it after a defined period of employment.
Use verified sponsorship data to guide your job search
Not every McAfee job listing will state sponsorship eligibility upfront. Migrate Mate surfaces verified sponsors so you can filter by real sponsorship history, helping you focus on McAfee roles that have an actual track record of Green Card support.
Prepare for the PERM labor certification process
Most McAfee Green Card filings go through PERM, which requires the company to document that no qualified U.S. worker is available. Understanding this process, and how your qualifications are documented, helps you support your employer's case effectively.
McAfee Green Card Visa Sponsorship: Frequently Asked Questions
Does McAfee sponsor Green Card visas?
Yes, McAfee sponsors Green Card visas for qualified employees, primarily through the EB-2 and EB-3 employment-based categories. Sponsorship is most common in technical and cybersecurity roles. The process typically begins after an employee has been with the company for some time and depends on the role, team budget, and business need.
Which roles and departments at McAfee are most likely to receive Green Card sponsorship?
McAfee's Green Card sponsorship is concentrated in technology-focused roles, software engineers, security researchers, data analysts, and cloud infrastructure specialists are among the positions most frequently sponsored. Corporate and operational roles are less commonly sponsored. If you're in cybersecurity or software development, your profile aligns well with McAfee's sponsorship patterns.
How do I navigate the Green Card application process at McAfee?
McAfee typically handles Green Card filings through the PERM labor certification route. The employer works with immigration counsel to document the job requirements, conduct a supervised recruitment process, and file with the Department of Labor before proceeding to the I-140 petition. Employees generally don't manage the process directly, but staying informed about each stage helps you plan your timeline.
How long does the Green Card process typically take at McAfee?
The timeline depends on your visa category and country of birth. The PERM and I-140 stages alone can take one to two years. For applicants from India or China, per-country backlogs in the EB-2 and EB-3 categories can extend the overall wait significantly beyond that. Starting the process as early as possible is the most important thing you can do.
How do I find Green Card jobs at McAfee that are open to sponsorship?
McAfee job postings don't always specify Green Card sponsorship eligibility, which makes it harder to filter for the right roles. Migrate Mate aggregates verified sponsorship data so you can identify McAfee positions backed by a real sponsorship history. Focusing on technical and engineering openings gives you the best odds, and reaching out to McAfee recruiters directly to confirm sponsorship availability before applying saves time.
What is the prevailing wage for Green Card sponsorship at McAfee?
Employers sponsoring a Green Card through the PERM labor certification process must pay at least the prevailing wage for the role. The Department of Labor determines this rate based on the specific job title, location, and experience level. The prevailing wage is locked in during the PERM filing and applies through the entire Green Card process. You can look up current rates using the DOL's OFLC Wage Search tool.