VP, Implementation Engineer (Remote - EST Hours)

Arch Capital Group•Raleigh, NC

1d•Remote

About The Position

With a company culture rooted in collaboration, expertise and innovation, we aim to promote progress and inspire our clients, employees, investors and communities to achieve their greatest potential. Our work is the catalyst that helps others achieve their goals. In short, We Enable Possibility℠. Strategic Analytics is a dynamic and growing team at Arch that drives innovation and transforms how the business operates. We build AI/ML-first products that change how Arch underwrites, services, and learn from its book — combining frontier LLMs, multi-agent orchestrations and traditional ML. Our mission is broad: agentic automation, decision support, AI/ML-driven insights, and the engineering required to make all of it production-grade. We have a strong track record of success. We aim to continue scaling these efforts and are seeking a VP Implementation Engineering to own the engineering vision, strategy, and production backbone for agentic and ML systems across Strategic Analytics. This role leads the implementation engineering (IE) function and owns the engineering practices, tooling, architectures and delivery pipelines that turn AI/ML models into reliable, scalable products — partnering closely with data scientists, data engineers, automation engineers, data visualization engineers, and platform teams. This role may sit remotely; being located near an Arch office is preferred but not required.

Requirements

10+ years of software engineering and/or automation engineering experience.
8+ years of people leadership experience, including leading other managers and leaders
Demonstrated experience building production grade AI and/or ML systems
Resilient problem solving — comfortable with ambiguous problems.
Exceptional communication and influence; able to operate across teams.

Nice To Haves

P&C insurance domain familiarity — underwriting, claims, or submission lifecycle.
Experience with complex orchestrations and microservice designs.
Cloud experience in Azure (preferred for our stack) and/or AWS; familiarity with private endpoints and enterprise-grade safeguards.
Experience leading multidisciplinary teams (onshore + offshore) for technology delivery.

Responsibilities

Translates AI strategy into scalable engineering execution—ensuring AI systems are safe, observable, cost-effective, compliant, and aligned with product and business outcomes.
Architect and govern AI/ML system designs including multi-agent flows, complex orchestrations, RAG, classical ML services and hybrid architectures.
Productionize AI/ML solutions into stable, low-latency, compliant, scalable applications.
Build, lead, and develop a multi-team AI/ML engineering organization—including managers and senior engineers—setting technical standards and a culture of rigorous, maintainable delivery.
Drive organization-wide alignment by influencing senior executives, peer engineering leaders, and product leaders; resolve prioritization conflicts and clarify accountability boundaries.
Spearhead the AI/ML Software Development Life Cycle (SDLC) balancing rapid experimentation with scalable, production-grade engineering standards.
Establish robust practices for versioning, deployment, CI/CD pipelines, logging, and automated rollback strategies.
Build and operate observability, monitoring, and alerting systems to track model drift, latency, throughput, and cloud computing cost governance.