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AI roles in machine learning, natural language processing, and computer vision regularly qualify for EB-2 and EB-3 green card sponsorship through PERM labor certification. Employers file on your behalf, certifying that no qualified U.S. worker is available for the role. Sponsorship leads to permanent residency, not a temporary status that needs renewing.
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Get Access To All JobsWe are seeking a strategic and hands-on Director, AI Engineering to lead the design, development, and scaling of Generative and Agentic AI systems that transform how our company operates and serves customers. This role focuses on building AI applications and reusable AI platform capabilities powered by large language models (LLMs), retrieval-augmented generation (RAG), Model Context Protocol (MCP), multi-agent systems, Agentic AI platforms, and modern AI/ML infrastructure across the enterprise for both internal and customer-facing use cases.
The ideal candidate combines deep expertise in a modern AI/ML stack with strong AI software engineering fundamentals, strong people leadership, and the ability to partner effectively across technology, data, product, operations, and business teams.
You will help define the AI engineering strategy and roadmap, lead a high-performing AI Engineering team, build and manage scalable AI products, establish standards for scalable and secure AI delivery, and help translate AI investments into measurable business outcomes.
Key Responsibilities
- Help to define and lead the execution of AI engineering strategy, target architecture, and roadmap for Generative and Agentic AI across the enterprise.
- Manage and motivate a high-performing team of AI engineers including coaching, mentoring, and scaling as required.
- Design, build, and oversee deployment of scalable LLM-powered applications and AI-native products for customer support, business operations, and internal productivity.
- Lead the development of AI agents, agentic platforms, and autonomous workflows capable of reasoning, planning, and executing multi-step tasks.
- Implement RAG architectures and pipelines to leverage proprietary data, internal knowledge bases, enterprise systems, and structured/unstructured content.
- Stand-up and evolve MCP servers, tool integrations, and multi-connectivity AI platforms to support secure orchestration across internal and external systems.
- Establish and oversee standards for prompt design, model evaluation, guardrails, observability, testing, and production readiness to ensure accuracy, resiliency, security, and regulatory compliance.
- Lead the design of MLOps and LLMOps pipelines for model lifecycle management, monitoring, continuous evaluation, and improvement.
- Leverage CI/CD and Agile methodologies for AI product development.
- Partner with architecture, security, legal, compliance, and data teams to ensure AI solutions meet requirements for privacy, governance, auditability, and responsible AI.
- Drive build vs. buy decisions, vendor selection, and collaboration with external partners, consultants, and internal teams to deliver scalable, high-quality AI solutions.
- Work closely with business leaders to identify and prioritize high-value use cases across the insurance business, process optimization, software development, and employee productivity.
- Define and track success metrics for adoption, quality, reliability, business impact, cost efficiency, and delivery velocity.
- Stay current with emerging advancements in AI/ML, Generative AI, agentic frameworks, model architectures, and enterprise AI engineering practices.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, or a related field. Graduate degrees are preferred, but not necessary.
- 10+ years of experience in software engineering, platform engineering, machine learning, or data science, with 2+ years in AI systems development.
- 5+ years of engineering leadership experience, including leading, mentoring, and scaling high-performing technical or AI/ML teams.
- Proven experience delivering LLM-powered applications and AI/ML systems into production at enterprise scale.
- Deep understanding of AI supporting infrastructure, security, testing, and monitoring/maintenance pipelines.
- Strong knowledge of Python, with hands-on experience with AI/ML-relevant packages and tools, such as NumPy, Pandas, SciPy, and Scikit-learn.
- Deep experience with modern LLM ecosystems and tools, such as:
- OpenAI / Anthropic / Google / open-source LLMs / other
- Claude Code, Codex, Cursor, GitHub Copilot, or similar
- LangChain, LlamaIndex, Crewai, or similar orchestration frameworks
- Experience building RAG pipelines, working with vector databases, and integrating AI with enterprise data and application environments.
- Experience designing and integrating APIs, MCP servers, scalable backend services, and AI platform components.
- Strong understanding of prompt engineering, embeddings, model evaluation, fine-tuning, and agent design and orchestration.
- Experience with cloud-based AI infrastructure and modern software delivery practices across AWS, Azure, or GCP. Familiarity with SnowFlake is preferred.
- Experience with MLOps / LLMOps, evaluation frameworks, observability, and continuous improvement for AI systems in production.
- Strong understanding of AI governance, security, privacy, risk controls, and compliance in enterprise or regulated environments.
- Experience influencing cross-functional stakeholders and communicating effectively with senior technology and business leaders.
- Ability to balance hands-on technical depth with strategic leadership, organizational development, and execution discipline.
Preferred Qualifications
- Advanced degree (M.Sc. or Ph.D.) in a relevant field.
- Experience building AI agents, agentic platforms, or autonomous workflows in production.
- Experience leading senior engineers, or technical leads in a scaled engineering organization.
- Familiarity with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, OpenAI Agents SDK, Semantic Kernel, LlamaIndex, Frontier, or similar.
- Experience with fine-tuning LLMs or parameter-efficient fine-tuning (PEFT) methods such as LoRA, or similar.
- Experience with multi-modal AI, document processing, semantic search, knowledge assistants, and enterprise workflow automation.
- Familiarity with Java, JavaScript, and enterprise application environments.
- Experience with vendor management, budgeting, and buy-vs-build evaluation for AI platforms and tooling.
- Experience implementing responsible AI, governance frameworks, security, observability, and guardrails at scale.
- Experience leading AI transformation initiatives across large, matrixed organizations.
- Experience in the insurance industry, finance, or other regulated industries, with exposure to fraud detection, risk analysis, claims, underwriting, servicing customers, or document intelligence.
What We Offer
- Opportunity to shape the future of AI engineering in the insurance industry
- Leadership role building next-generation Generative and Agentic AI capabilities
- High-impact work across customer, employee, and operational experiences
- Collaborative environment with technology, data, and business leaders
- Competitive compensation and benefits
- Hybrid work environment in Boston
- Opportunity to build both enterprise AI platforms and high-value AI products that drive measurable business results
Impact
In this key role, you will shape how AI is engineered, governed, and scaled across the enterprise. You will help build an AI-first insurance company, leveraging Generative and Agentic AI to improve decision-making, streamline operations, accelerate service, and deliver better experiences for customers, business stakeholders.
Salary Range:
The pay range for this position is $205,000 to $282,000 annually. Actual compensation will vary based on multiple factors, including employee knowledge and experience, role scope, business needs, geographical location, and internal equity.
Perks and Benefits:
- 4 weeks accrued paid time off + 9 paid national holidays per year
- Free onsite gym at our Boston Location
- Tuition Reimbursement
- Low cost and excellent coverage health insurance options that start on Day 1 (medical, dental, vision)
- Robust health and wellness program and fitness reimbursements
- Auto and home insurance discounts
- Matching gift opportunities
- Annual 401(k) Employer Contribution (up to 7.5% of your base salary)
- Various Paid Family leave options including Paid Parental Leave
- Resources to promote Professional Development (LinkedIn Learning and licensure assistance)
- Convenient location directly across from South Station and Pre-Tax Commuter Benefits
About the Company
The Plymouth Rock Company and its affiliated group of companies write and manage over $2.3 billion in personal and commercial auto and homeowner's insurance throughout the Northeast and mid-Atlantic, where we have built an unparalleled reputation for service. We continuously invest in technology, our employees thrive in our empowering environment, and our customers are among the most loyal in the industry. The Plymouth Rock group of companies employs more than 1,900 people and is headquartered in Boston, Massachusetts. Plymouth Rock Assurance Corporation holds an A.M. Best rating of “A-/Excellent”
We are seeking a strategic and hands-on Director, AI Engineering to lead the design, development, and scaling of Generative and Agentic AI systems that transform how our company operates and serves customers. This role focuses on building AI applications and reusable AI platform capabilities powered by large language models (LLMs), retrieval-augmented generation (RAG), Model Context Protocol (MCP), multi-agent systems, Agentic AI platforms, and modern AI/ML infrastructure across the enterprise for both internal and customer-facing use cases.
The ideal candidate combines deep expertise in a modern AI/ML stack with strong AI software engineering fundamentals, strong people leadership, and the ability to partner effectively across technology, data, product, operations, and business teams.
You will help define the AI engineering strategy and roadmap, lead a high-performing AI Engineering team, build and manage scalable AI products, establish standards for scalable and secure AI delivery, and help translate AI investments into measurable business outcomes.
Key Responsibilities
- Help to define and lead the execution of AI engineering strategy, target architecture, and roadmap for Generative and Agentic AI across the enterprise.
- Manage and motivate a high-performing team of AI engineers including coaching, mentoring, and scaling as required.
- Design, build, and oversee deployment of scalable LLM-powered applications and AI-native products for customer support, business operations, and internal productivity.
- Lead the development of AI agents, agentic platforms, and autonomous workflows capable of reasoning, planning, and executing multi-step tasks.
- Implement RAG architectures and pipelines to leverage proprietary data, internal knowledge bases, enterprise systems, and structured/unstructured content.
- Stand-up and evolve MCP servers, tool integrations, and multi-connectivity AI platforms to support secure orchestration across internal and external systems.
- Establish and oversee standards for prompt design, model evaluation, guardrails, observability, testing, and production readiness to ensure accuracy, resiliency, security, and regulatory compliance.
- Lead the design of MLOps and LLMOps pipelines for model lifecycle management, monitoring, continuous evaluation, and improvement.
- Leverage CI/CD and Agile methodologies for AI product development.
- Partner with architecture, security, legal, compliance, and data teams to ensure AI solutions meet requirements for privacy, governance, auditability, and responsible AI.
- Drive build vs. buy decisions, vendor selection, and collaboration with external partners, consultants, and internal teams to deliver scalable, high-quality AI solutions.
- Work closely with business leaders to identify and prioritize high-value use cases across the insurance business, process optimization, software development, and employee productivity.
- Define and track success metrics for adoption, quality, reliability, business impact, cost efficiency, and delivery velocity.
- Stay current with emerging advancements in AI/ML, Generative AI, agentic frameworks, model architectures, and enterprise AI engineering practices.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, or a related field. Graduate degrees are preferred, but not necessary.
- 10+ years of experience in software engineering, platform engineering, machine learning, or data science, with 2+ years in AI systems development.
- 5+ years of engineering leadership experience, including leading, mentoring, and scaling high-performing technical or AI/ML teams.
- Proven experience delivering LLM-powered applications and AI/ML systems into production at enterprise scale.
- Deep understanding of AI supporting infrastructure, security, testing, and monitoring/maintenance pipelines.
- Strong knowledge of Python, with hands-on experience with AI/ML-relevant packages and tools, such as NumPy, Pandas, SciPy, and Scikit-learn.
- Deep experience with modern LLM ecosystems and tools, such as:
- OpenAI / Anthropic / Google / open-source LLMs / other
- Claude Code, Codex, Cursor, GitHub Copilot, or similar
- LangChain, LlamaIndex, Crewai, or similar orchestration frameworks
- Experience building RAG pipelines, working with vector databases, and integrating AI with enterprise data and application environments.
- Experience designing and integrating APIs, MCP servers, scalable backend services, and AI platform components.
- Strong understanding of prompt engineering, embeddings, model evaluation, fine-tuning, and agent design and orchestration.
- Experience with cloud-based AI infrastructure and modern software delivery practices across AWS, Azure, or GCP. Familiarity with SnowFlake is preferred.
- Experience with MLOps / LLMOps, evaluation frameworks, observability, and continuous improvement for AI systems in production.
- Strong understanding of AI governance, security, privacy, risk controls, and compliance in enterprise or regulated environments.
- Experience influencing cross-functional stakeholders and communicating effectively with senior technology and business leaders.
- Ability to balance hands-on technical depth with strategic leadership, organizational development, and execution discipline.
Preferred Qualifications
- Advanced degree (M.Sc. or Ph.D.) in a relevant field.
- Experience building AI agents, agentic platforms, or autonomous workflows in production.
- Experience leading senior engineers, or technical leads in a scaled engineering organization.
- Familiarity with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, OpenAI Agents SDK, Semantic Kernel, LlamaIndex, Frontier, or similar.
- Experience with fine-tuning LLMs or parameter-efficient fine-tuning (PEFT) methods such as LoRA, or similar.
- Experience with multi-modal AI, document processing, semantic search, knowledge assistants, and enterprise workflow automation.
- Familiarity with Java, JavaScript, and enterprise application environments.
- Experience with vendor management, budgeting, and buy-vs-build evaluation for AI platforms and tooling.
- Experience implementing responsible AI, governance frameworks, security, observability, and guardrails at scale.
- Experience leading AI transformation initiatives across large, matrixed organizations.
- Experience in the insurance industry, finance, or other regulated industries, with exposure to fraud detection, risk analysis, claims, underwriting, servicing customers, or document intelligence.
What We Offer
- Opportunity to shape the future of AI engineering in the insurance industry
- Leadership role building next-generation Generative and Agentic AI capabilities
- High-impact work across customer, employee, and operational experiences
- Collaborative environment with technology, data, and business leaders
- Competitive compensation and benefits
- Hybrid work environment in Boston
- Opportunity to build both enterprise AI platforms and high-value AI products that drive measurable business results
Impact
In this key role, you will shape how AI is engineered, governed, and scaled across the enterprise. You will help build an AI-first insurance company, leveraging Generative and Agentic AI to improve decision-making, streamline operations, accelerate service, and deliver better experiences for customers, business stakeholders.
Salary Range:
The pay range for this position is $205,000 to $282,000 annually. Actual compensation will vary based on multiple factors, including employee knowledge and experience, role scope, business needs, geographical location, and internal equity.
Perks and Benefits:
- 4 weeks accrued paid time off + 9 paid national holidays per year
- Free onsite gym at our Boston Location
- Tuition Reimbursement
- Low cost and excellent coverage health insurance options that start on Day 1 (medical, dental, vision)
- Robust health and wellness program and fitness reimbursements
- Auto and home insurance discounts
- Matching gift opportunities
- Annual 401(k) Employer Contribution (up to 7.5% of your base salary)
- Various Paid Family leave options including Paid Parental Leave
- Resources to promote Professional Development (LinkedIn Learning and licensure assistance)
- Convenient location directly across from South Station and Pre-Tax Commuter Benefits
About the Company
The Plymouth Rock Company and its affiliated group of companies write and manage over $2.3 billion in personal and commercial auto and homeowner's insurance throughout the Northeast and mid-Atlantic, where we have built an unparalleled reputation for service. We continuously invest in technology, our employees thrive in our empowering environment, and our customers are among the most loyal in the industry. The Plymouth Rock group of companies employs more than 1,900 people and is headquartered in Boston, Massachusetts. Plymouth Rock Assurance Corporation holds an A.M. Best rating of “A-/Excellent”
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Get Access To All JobsTips for Finding Green Card Sponsorship in AI
Document your AI specialization before applying
PERM requires your employer to define a specific job duty set tied to your credentials. Compile evidence of your machine learning frameworks, published models, or research outputs now so your employer's labor certification accurately reflects the role you'll actually perform.
Target employers with active PERM filing history
Not every company that hires AI engineers has filed PERM petitions before. Use Migrate Mate to filter for employers with documented green card sponsorship history in AI and data science roles, so you're not the first person they've ever sponsored.
Clarify whether your role qualifies for EB-2 or EB-3
EB-2 requires a master's degree or its equivalent in a directly related field. If your AI role requires only a bachelor's degree, it may be classified EB-3 instead, which affects your priority date timeline depending on your country of birth.
Negotiate PERM sponsorship into your offer letter
Employers are not required to sponsor green cards, and many AI teams treat it as optional. Ask explicitly during the offer stage whether PERM is included in the employment package, and get the timeline commitment in writing before you sign.
Verify your prevailing wage classification early
PERM locks your employer into paying at least the DOL prevailing wage for your specific SOC code and work location. Check the OFLC Wage Search for your AI job title before accepting an offer to confirm the wage level aligns with what the company is prepared to pay.
Use O*NET to confirm specialty occupation alignment
USCIS evaluates whether your AI role qualifies as a specialty occupation requiring a specific degree. Cross-check your job title and duties against the O*NET occupation profile to identify the degree field your employer should list on the PERM application.
Green Card AI: Frequently Asked Questions
Do AI jobs commonly qualify for EB-2 or EB-3 green card sponsorship?
Most AI engineering and research roles qualify for EB-2 because they require a master's degree or its equivalent in computer science, mathematics, or a related technical field. Roles that require only a bachelor's degree typically fall under EB-3. Either category goes through PERM labor certification, where the employer must show no qualified U.S. worker is available for the specific position.
How does green card sponsorship differ from H-1B for AI professionals?
H-1B visa is a temporary status that requires renewal every three years and, for most nationalities, subjects you to an annual lottery. PERM-based green card sponsorship has no lottery and leads to permanent residency. The tradeoff is time: PERM labor certification, I-140 approval, and adjustment of status together can take two to four years for most nationalities, longer for Indian and Chinese nationals due to priority date backlogs.
What does the PERM labor certification process involve for an AI role?
Your employer files a PERM application with DOL certifying that they advertised the AI position, found no minimally qualified U.S. applicants, and are offering at least the prevailing wage for that SOC code and location. The recruitment period typically spans 30 to 60 days. Once DOL certifies the application, your employer files an I-140 immigrant petition with USCIS to formally establish your priority date.
How do I find AI employers who will actually sponsor a green card?
Use Migrate Mate to search for AI and machine learning roles filtered by employers with confirmed green card sponsorship history. Many job postings don't advertise PERM sponsorship upfront, so filtering by verified filing history saves significant time and helps you focus your applications on companies that have completed the process before rather than those encountering it for the first time.
Can my employer start my PERM process while I'm on an H-1B?
Yes, and starting early is strongly advisable. PERM labor certification can begin at any point during your H-1B employment. If your I-140 is approved and your priority date is current, you can file for adjustment of status before your H-1B expires. Filing PERM while you're still in early H-1B status gives you the most flexibility if your priority date takes longer than expected to become current.