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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 Cloud AI Architect
Document your cloud AI specializations early
Gather degree transcripts, professional certifications (AWS, GCP, Azure AI), and project portfolios before applying. PERM requires your employer to verify your credentials match the job duties, so clean documentation speeds up labor certification significantly.
Target employers with active PERM filing history
Use the OFLC Wage Search to filter DOL disclosure data for Cloud AI Architect or related SOC codes. Employers who have filed PERM for these roles before are far more likely to sponsor again than those without any filing history.
Clarify the EB-2 versus EB-3 track before accepting an offer
Ask whether the role will be classified under EB-2 (requiring a master's degree or equivalent) or EB-3 (bachelor's degree sufficient). Your priority date and wait time can differ substantially depending on your nationality and which category the employer files under.
Search for sponsoring employers on Migrate Mate
Filter Cloud AI Architect openings on Migrate Mate by green card sponsorship history to surface employers who have completed PERM filings for similar roles. This cuts out the guesswork of cold-applying to companies that won't sponsor.
Confirm the prevailing wage tier in your job offer
DOL assigns a wage level (I through IV) on the PERM-certified LCA. Cloud AI Architect roles typically land at Level III or IV. If your offered salary sits below the certified prevailing wage, USCIS can deny the I-140 petition even after PERM is approved.
Understand what changes between PERM approval and I-485 filing
PERM certification does not lock in your green card. Your employer must still file the I-140 petition, and you can't file the I-485 adjustment of status until a visa number is available in your category and your priority date becomes current.
Green Card Cloud AI Architect: Frequently Asked Questions
Does a Cloud AI Architect role qualify for EB-2 green card sponsorship?
Yes, if the position genuinely requires a master's degree or higher in computer science, artificial intelligence, or a closely related field. Employers can also make an EB-2 case for candidates with a bachelor's degree plus five years of progressive experience demonstrating specialized expertise. The job duties in the PERM application must support whichever credential standard the employer selects.
How does green card sponsorship differ from H-1B for Cloud AI Architects?
H-1B visa is a temporary status tied to a specific employer and subject to the annual lottery cap. EB-2 and EB-3 green card sponsorship leads to permanent residency with no cap concerns at the petition level, though visa number availability depends on your birth country. The PERM labor certification process also adds six to twelve months before your employer can file the I-140, making the total timeline longer than a standard H-1B transfer.
Which employers actually sponsor green cards for Cloud AI Architect positions?
Cloud-native and enterprise technology employers with large AI infrastructure teams file the most PERM applications for these roles. Financial services firms, healthcare systems expanding their AI platforms, and federal contractors also sponsor regularly. Use Migrate Mate to filter job listings by verified green card sponsorship history so you're targeting employers who have completed the PERM process for Cloud AI Architect or adjacent titles.
What is the O*NET classification for Cloud AI Architect roles and why does it matter for PERM?
O*NET classifies Cloud AI Architect work closest to Software Developers and Architects (SOC 15-1252) or Computer and Information Research Scientists (SOC 15-1221), both in Job Zone 5 requiring extensive preparation. The SOC code your employer selects determines the DOL prevailing wage level on the PERM application. A mismatched SOC code can result in an audit or denial from OFLC, delaying your entire green card timeline.
Can I switch employers after PERM is approved but before my green card is issued?
If your I-140 petition has been approved for more than 180 days and your I-485 is pending, AC21 portability lets you change to a new employer in the same or similar occupational classification without losing your priority date. For Cloud AI Architect roles, a move to a comparable senior cloud or AI engineering position typically satisfies the same-or-similar standard, but your new employer should review the specifics before you make the switch.