Director, Artificial Intelligence Engineering

Plymouth Rock Assurance•Boston, MA

1d•Hybrid

About The Position

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 defining 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.

Requirements

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics, or a related field.
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.
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.

Nice To Haves

Graduate degrees are preferred, but not necessary.
Familiarity with SnowFlake is preferred.
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.

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.

Benefits

Competitive compensation 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