AI Platform Engineer Jobs in USA with Visa Sponsorship
AI Platform Engineers are among the most actively sponsored roles in tech right now. Employers filing H-1B visa petitions for this title typically require a degree in computer science, software engineering, or a related field, and USCIS consistently classifies the role as a specialty occupation. For detailed occupation requirements, see the O*NET profile.
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We are building an AI Engineering function to enable productivity and agentic capabilities across the firm, for end users, developers, and business teams.
As a Senior AI Platform Engineer, you will design and own the shared platform that powers AI systems firm-wide: inference services, agentic platforms, developer tooling, and observability.
This is a financial services environment where data protection, auditability, and regulatory compliance are foundational requirements. You will ensure that AI capabilities are secure by default, auditable end-to-end, and easy for engineering teams to adopt.
You will report to the Head of AI Engineering and partner closely with Security Engineering, AI Integration/Application teams, and core infrastructure groups.
Responsibilities
Platform Infrastructure
- Design, build, and operate the core AI platform, including managed LLM inference services (Amazon Bedrock and related), model access management, versioning, and routing across foundation models
- Design and operate shared integration layers, including MCP servers, an MCP registry/gateway, and authorization services that connect AI platforms with core firm systems
- Design and operate AI productivity data pipelines and dashboards for usage, cost, and adoption metrics
- Design the infrastructure that supports AI-assisted developer tooling (Linux VDI environments), office productivity integrations (M365/Excel), and autonomous agent frameworks
- Develop standardized inference and agentic AI platforms that teams can adopt across use cases, including reusable components for RAG, vector databases, and model integration patterns
Security & Guardrails
- Partner with Security Engineering to embed security controls across the full AI lifecycle
- Design, with the AI Security Engineer and infrastructure/platform teams, the controls that prevent destructive agent actions: filesystem permissions, IAM policies, network allowlists, sandbox configurations, and execution-time policy enforcement
- Architect a default-deny posture: agents and tools access only explicitly permitted resources, with no ability to modify or delete production data unless specifically authorized through a human-approval workflow
- Implement pre-execution guardrails (hooks, policy engines) that intercept and validate agent actions before they run
- Ensure AI workloads operate within the corporate network boundary: VPC endpoints, PrivateLink, no public internet egress for inference traffic
Enablement & Scale
- Build self-service onboarding so teams can consume AI platform services with appropriate access controls
- Design systems that enable cost-effective operation of AI workloads, including quota management and chargeback visibility
- Operate firm-wide AI applications and centrally managed AI services
- Define reference architectures and patterns that other engineering teams use to build on the platform
Qualifications
- 10+ years as an infrastructure, platform, or systems engineer, with demonstrated experience building and operating shared services consumed by multiple teams, on-premises and on AWS
- Strong expertise in AWS Bedrock (inference / agent core) and Azure OpenAI
- Strong expertise in designing and implementing MCP registries, gateways, servers and Authorization flows
- Hands-on experience supporting LLM-based workloads in production environments
- Experience designing and enforcing AI security controls at the platform layer in a regulated or security-sensitive environment
- Track record of building production-quality agentic AI patterns: tool use, function calling, MCP gateway/servers, retrieval-augmented generation, human-in-the-loop workflows
- Track record of building production-quality platforms and developer-facing services, with emphasis on usability, standardization, and reliability
- Strong written and verbal communication skills, with the ability to work effectively across security, application, and infrastructure teams
Preferred Qualifications
- Experience in financial services, healthcare, or another heavily regulated industry
- Experience with Microsoft M365 Copilot / Copilot Agents
- Experience building observability pipelines (Splunk, ELK, Datadog, or Grafana)
- Familiarity with containerized and Kubernetes-based environments
- Experience with model fine-tuning workflows and ML lifecycle tooling
- Familiarity with DLP tooling and data classification frameworks
The base salary range for this position is $200,000 - $325,000 per year.
Arrowstreet Capital operates a robust talent acquisition program, and we also seek to compensate and reward our employees competitively within our industry and in line with our merit-based culture. Our approach to total compensation includes base salaries and annual discretionary bonuses, as well as a robust benefits package. The determination of a successful candidate’s base salary placement within the listed range will vary based on the candidate’s relevant experience and qualifications (which may also include relevant certifications, credentials and other education), the job responsibilities and scope, the commensurate resulting level of the position and other relevant factors. The listed range is also an estimate, and additional information regarding base salary and other elements of total compensation offered by Arrowstreet Capital to successful applicants will be communicated during the recruitment process.
Arrowstreet Capital is a Boston-based systematic investment firm that manages global equity portfolios for institutional investors around the world.
All qualified applicants will receive consideration for employment without regard to sex, race, color, religion, national origin, ancestry, genetic information, age, pregnancy, medical condition, disability, veteran or military status, marital status or any other characteristic protected by federal, state, or local law.
Arrowstreet Capital is committed to working with and providing reasonable accommodations for qualified individuals with disabilities and disabled veterans. If you need a reasonable accommodation for any part of the employment process due to a disability, contact us to discuss the nature of your request and contact information.
Job Description
We are building an AI Engineering function to enable productivity and agentic capabilities across the firm, for end users, developers, and business teams.
As a Senior AI Platform Engineer, you will design and own the shared platform that powers AI systems firm-wide: inference services, agentic platforms, developer tooling, and observability.
This is a financial services environment where data protection, auditability, and regulatory compliance are foundational requirements. You will ensure that AI capabilities are secure by default, auditable end-to-end, and easy for engineering teams to adopt.
You will report to the Head of AI Engineering and partner closely with Security Engineering, AI Integration/Application teams, and core infrastructure groups.
Responsibilities
Platform Infrastructure
- Design, build, and operate the core AI platform, including managed LLM inference services (Amazon Bedrock and related), model access management, versioning, and routing across foundation models
- Design and operate shared integration layers, including MCP servers, an MCP registry/gateway, and authorization services that connect AI platforms with core firm systems
- Design and operate AI productivity data pipelines and dashboards for usage, cost, and adoption metrics
- Design the infrastructure that supports AI-assisted developer tooling (Linux VDI environments), office productivity integrations (M365/Excel), and autonomous agent frameworks
- Develop standardized inference and agentic AI platforms that teams can adopt across use cases, including reusable components for RAG, vector databases, and model integration patterns
Security & Guardrails
- Partner with Security Engineering to embed security controls across the full AI lifecycle
- Design, with the AI Security Engineer and infrastructure/platform teams, the controls that prevent destructive agent actions: filesystem permissions, IAM policies, network allowlists, sandbox configurations, and execution-time policy enforcement
- Architect a default-deny posture: agents and tools access only explicitly permitted resources, with no ability to modify or delete production data unless specifically authorized through a human-approval workflow
- Implement pre-execution guardrails (hooks, policy engines) that intercept and validate agent actions before they run
- Ensure AI workloads operate within the corporate network boundary: VPC endpoints, PrivateLink, no public internet egress for inference traffic
Enablement & Scale
- Build self-service onboarding so teams can consume AI platform services with appropriate access controls
- Design systems that enable cost-effective operation of AI workloads, including quota management and chargeback visibility
- Operate firm-wide AI applications and centrally managed AI services
- Define reference architectures and patterns that other engineering teams use to build on the platform
Qualifications
- 10+ years as an infrastructure, platform, or systems engineer, with demonstrated experience building and operating shared services consumed by multiple teams, on-premises and on AWS
- Strong expertise in AWS Bedrock (inference / agent core) and Azure OpenAI
- Strong expertise in designing and implementing MCP registries, gateways, servers and Authorization flows
- Hands-on experience supporting LLM-based workloads in production environments
- Experience designing and enforcing AI security controls at the platform layer in a regulated or security-sensitive environment
- Track record of building production-quality agentic AI patterns: tool use, function calling, MCP gateway/servers, retrieval-augmented generation, human-in-the-loop workflows
- Track record of building production-quality platforms and developer-facing services, with emphasis on usability, standardization, and reliability
- Strong written and verbal communication skills, with the ability to work effectively across security, application, and infrastructure teams
Preferred Qualifications
- Experience in financial services, healthcare, or another heavily regulated industry
- Experience with Microsoft M365 Copilot / Copilot Agents
- Experience building observability pipelines (Splunk, ELK, Datadog, or Grafana)
- Familiarity with containerized and Kubernetes-based environments
- Experience with model fine-tuning workflows and ML lifecycle tooling
- Familiarity with DLP tooling and data classification frameworks
The base salary range for this position is $200,000 - $325,000 per year.
Arrowstreet Capital operates a robust talent acquisition program, and we also seek to compensate and reward our employees competitively within our industry and in line with our merit-based culture. Our approach to total compensation includes base salaries and annual discretionary bonuses, as well as a robust benefits package. The determination of a successful candidate’s base salary placement within the listed range will vary based on the candidate’s relevant experience and qualifications (which may also include relevant certifications, credentials and other education), the job responsibilities and scope, the commensurate resulting level of the position and other relevant factors. The listed range is also an estimate, and additional information regarding base salary and other elements of total compensation offered by Arrowstreet Capital to successful applicants will be communicated during the recruitment process.
Arrowstreet Capital is a Boston-based systematic investment firm that manages global equity portfolios for institutional investors around the world.
All qualified applicants will receive consideration for employment without regard to sex, race, color, religion, national origin, ancestry, genetic information, age, pregnancy, medical condition, disability, veteran or military status, marital status or any other characteristic protected by federal, state, or local law.
Arrowstreet Capital is committed to working with and providing reasonable accommodations for qualified individuals with disabilities and disabled veterans. If you need a reasonable accommodation for any part of the employment process due to a disability, contact us to discuss the nature of your request and contact information.
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Get Access To All JobsTips for Finding Visa Sponsorship as an AI Platform Engineer
Target companies with existing AI infrastructure buildouts
Large tech firms, cloud providers, and AI-native startups are investing heavily in platform teams right now. These organizations have established immigration counsel and sponsor H-1B, O-1, and L-1 visas regularly, making them far more likely to move quickly on sponsorship.
Align your degree field to the role as precisely as possible
USCIS scrutinizes specialty occupation status for platform engineering roles. A degree in computer science, electrical engineering, or information systems strengthens your petition. A business or unrelated degree will likely trigger an RFE and require additional documentation to overcome.
Document your MLOps and infrastructure work with measurable outcomes
Sponsoring employers need to justify the specialty occupation classification. Quantified examples of platforms you built, models you deployed at scale, or infrastructure you designed help immigration counsel build a stronger petition and reduce the risk of USCIS challenges.
Understand the difference between cap-subject and cap-exempt employers
Universities, nonprofit research institutions, and government-affiliated labs are cap-exempt, meaning they can file H-1B petitions year-round without entering the lottery. For AI platform roles specifically, national labs and university research centers are worth targeting if lottery odds concern you.
Consider the O-1A if your work has produced notable recognition
AI platform engineers who have published research, presented at major conferences, or led high-profile open source projects may qualify for the O-1A extraordinary ability visa. It bypasses the H-1B lottery entirely and has no annual cap or nationality restrictions.
Filter your job search to verified sponsoring employers from the start
Applying broadly wastes time on roles where sponsorship was never an option. Migrate Mate lists jobs from employers actively willing to sponsor, so you can focus your applications on companies that have already confirmed they will support the visa process.
Frequently Asked Questions
Is AI Platform Engineer considered a specialty occupation for H-1B purposes?
Yes, in nearly all cases. USCIS evaluates specialty occupation status based on whether the role normally requires a bachelor's degree or higher in a specific field. AI Platform Engineer roles consistently meet this standard, as employers require computer science, software engineering, or a closely related degree. Roles that blend platform work with vague generalist responsibilities can occasionally draw an RFE, so the job description language your employer uses in the LCA matters.
What degree do I need to get H-1B sponsorship as an AI Platform Engineer?
A bachelor's degree or higher in computer science, software engineering, electrical engineering, or information systems is the standard requirement. Some employers also accept degrees in applied mathematics or data science if your coursework directly supports the platform engineering functions of the role. Unrelated degrees, such as business or liberal arts, will create specialty occupation problems unless you can supplement with substantial relevant coursework or a related advanced degree.
How competitive is the H-1B lottery for AI Platform Engineers compared to other tech roles?
The lottery itself applies equally to all cap-subject petitions regardless of job title. What differs is employer strategy. Many companies hiring AI Platform Engineers file early, use premium processing, and work with experienced immigration counsel, which doesn't change lottery odds but does reduce delays after selection. If you're concerned about lottery risk, cap-exempt employers and O-1A eligibility are worth evaluating before the registration window opens.
Can I switch employers on an H-1B as an AI Platform Engineer without losing my status?
Yes. Under H-1B portability, you can start working for a new employer as soon as they file an H-1B transfer petition, without waiting for approval, provided you've maintained valid status. The new employer needs to file a new LCA certified by the Department of Labor for the specific worksite and wage level. For AI platform roles, this process is generally straightforward since the specialty occupation classification is well-established.
Where can I find AI Platform Engineer jobs that explicitly offer visa sponsorship?
Migrate Mate is built specifically for this. Every listing on Migrate Mate is from an employer willing to sponsor, so you're not wasting applications on roles that will decline at the offer stage. You can browse AI Platform Engineer roles directly and filter by visa type, which is particularly useful if you're weighing H-1B cap-subject employers against cap-exempt options or companies open to O-1A sponsorship.
What is the prevailing wage requirement for sponsored AI Platform Engineer jobs?
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.
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