The client is seeking an accomplished VP of Software Engineering and AI to lead an on-site software organization through its next stage of modernization, with a strong emphasis on turning AI ambitions into working technology. This is an executive role for a builder who remains deeply involved in architecture, engineering decisions, and delivery, and who can personally help move AI capabilities into production. The VP will shape how the organization applies AI, evolves an established technology environment, and develops the engineering capabilities needed for future growth.
Key responsibilities include providing executive ownership of software engineering at a meaningful point of technology transformation, driving practical AI initiatives from technical evaluation and prototyping through integration, deployment, measurement, and continuous improvement, and improving products, workflows, decision-making, and operational efficiency. The role includes modernization of an established custom software environment, including .NET applications and related systems, while protecting the reliability of business-critical operations. The VP will also set engineering priorities across AI adoption, platform modernization, technical debt, reliability, security, and ongoing business needs, while partnering with senior business, product, and data leaders to translate priorities into executable initiatives and measurable outcomes.
The client expects proven success implementing AI in production environments through personally built and operationalized solutions, along with hands-on engineering leadership that balances organizational direction with technical execution. The role requires strong experience with .NET and enterprise software development, the ability to inherit and modernize unfamiliar legacy or custom applications, and knowledge of software architecture, APIs, distributed applications, cloud technologies, data integration, and contemporary engineering practices. Additional expectations include experience strengthening engineering practices across software delivery, testing, deployment, observability, security, and operational readiness, evaluating AI platforms and development tools based on practical implementation needs and business value, and developing engineering leaders with a culture focused on experimentation, accountability, continuous learning, and execution.