AI Technology Leader Job Glen Allen area,Virginia USA,IT/Tech

Berkley Technology Services (BTS) is a $14B commercial and specialty insurance provider delivering innovative, data‑driven risk solutions. As part of an enterprise transformation, BTS is investing in next‑generation AI technologies to reimagine how we operate, serve customers, and manage risk.

We are seeking a Strategic Enterprise AI Technology Leader to lead the architecture, platform strategy, technology standardization and enablement of Generative and Agentic AI capabilities across the organization. This leadership role focuses on incubating ideas for automation, implementing them with selected operating units, and working with other application development and data teams to scale.

Key Responsibilities

1. Enterprise AI Technology Strategy
- Define and evolve the enterprise‑wide technology strategy for Generative and Agentic AI, aligned with business goals and operational priorities.
- Partner with the Corporate AI Leader to translate business needs into scalable AI capabilities and reusable platform components.
- Serve as a strategic advisor to senior leadership on AI platform direction, architectural decisions, and emerging technologies.
2. Platform Architecture & Enablement
- Lead the design of a modular, secure, and scalable AI architecture that supports generative and agentic AI use cases across underwriting, claims, policy servicing, actuarial, finance, HR, IT and customer engagement.
- Define reference architectures, reusable components, and integration patterns for AI agents, orchestration frameworks, and LLM‑based services.
- Ensure alignment with enterprise architecture, cloud strategy, and data platform capabilities.
3. Team Leadership
- Lead a small, high‑impact team of AI engineers and product managers responsible for building and enabling AI capabilities across the enterprise. Some members will work in matrix reporting relationships.
- Establish foundational practices, delivery models, and team culture as the function matures.
- Provide strategic direction, coaching, and prioritization to ensure delivery of high‑value solutions.
4. AI Ecosystem & Technology Selection
- Evaluate and recommend enterprise‑grade AI platforms, orchestration frameworks (e.g., Lang Chain, Semantic Kernel), vector databases, and agentic AI toolkits.
- Guide development of a shared AI services layer (e.g., prompt libraries, RAG pipelines, agent orchestration) to accelerate delivery and reuse.
- Stay current with the evolving AI technology landscape and assess applicability to the insurance domain.
- Review technical and business proposals and guide teams to land on optimal AI solutions.
5. Governance & Risk Collaboration
- Collaborate with the AI Governance team to ensure AI solutions are designed and deployed in compliance with regulatory, ethical, and risk management standards.
- Contribute to the development of policies and frameworks for responsible AI, including transparency, explainability, and human oversight.
- Ensure that technology decisions support auditability, traceability, and model lifecycle management.
- Discern between Gen AI related risks, operational automation/business risks and evangelize the right adoption framework.
6. Cross‑Functional Alignment
- Partner with enterprise architects, infrastructure teams, and applications teams to ensure seamless integration of AI into existing systems and workflows.
- Partner with Corporate AI Leader to ensure business priorities are reflected in the implementation plans.

Qualifications

Education & Experience
- Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field; MBA or equivalent business education is a plus.
- 12+ years of experience in enterprise technology leadership roles, with at least 3 years focused on AI/automation strategy, architecture, or platform enablement.
- Experience in commercial or specialty insurance, financial services, or other regulated industries is strongly preferred.
Technology & Domain Expertise
- Strong understanding of Generative AI (LLMs, RAG, prompt engineering) and Agentic AI (multi‑agent systems, autonomous workflows) from a platform and architecture perspective.
- Familiarity with enterprise AI platforms (e.g., Azure OpenAI, AWS Bedrock,…