Vice President (AI Solutions Architect)

Golub Capital•Chicago, NY

About The Position

Golub Capital’s Technology Solutions Department designs and delivers technology solutions that improve efficiency, mitigate risk and grow revenue. It manages the Firm’s cybersecurity program and focuses on technical excellence through application design, data integration and analytics, infrastructure availability, and information security. The department works with Firm leadership and business unit heads to align technology plans with business objectives. The Vice President, Applied AI Solutions will lead the practical application of artificial intelligence across the Firm. In partnership with business leaders and the AI Business Transformation Team, this role identifies and shapes high-impact AI opportunities, translates business needs into scalable solution designs, and provides architectural guidance from initial concept through implementation and production release. The role establishes reusable patterns, standards and capabilities that support AI adoption while aligning solutions with business objectives, security requirements, operational standards and long-term outcomes. The position requires deep expertise in applied generative AI, enterprise architecture, process design and the Firm’s technology ecosystem. The successful candidate will assess when to use existing capabilities, extend approved platforms, build custom solutions or recommend non-AI approaches; design secure and scalable AI and agentic architectures; guide engineering teams through delivery; and develop reusable architecture patterns and evaluation methods. The role also requires staying informed about AI developments and translating relevant advances into practical guidance for the Firm.

Requirements

Bachelor’s degree in computer science, engineering, information systems, data science or a related field.
At least 10 years of experience in solution architecture, software engineering, AI/ML engineering, enterprise application design, technology consulting or related disciplines, including guiding complex solutions from discovery through production.
Demonstrated ability to partner with business stakeholders, uncover underlying needs, translate ambiguous problems into clear solution designs, challenge assumptions and build consensus across business and technology teams.
Deep expertise in applied generative AI architecture, including large language models, agentic and multi-agent patterns, retrieval-augmented generation, context engineering, knowledge grounding, prompt and tool design, orchestration and human-in-the-loop controls.
Strong command of modern AI architecture patterns and open standards, including Model Context Protocol and related approaches for connecting models, agents, tools, data and enterprise services.
Demonstrated experience designing hybrid solutions that combine probabilistic AI with deterministic software, workflows and rules to meet requirements for reliability, control and explainability.
Experience assessing enterprise technology landscapes, mapping use cases to existing platform capabilities and recommending reusable, standards-aligned extensions where needed.
Demonstrated experience developing implementation-ready architectures and guiding solutions through engineering handoff, build, testing and production release, including resolving design questions as technical findings emerge.
Deep knowledge of Microsoft’s AI and data ecosystem, including Azure OpenAI, Azure AI Foundry, Microsoft Copilot Studio, Microsoft 365 Copilot, Power Platform and Fabric, and the judgment to determine when to configure, extend or build beyond existing capabilities.
Strong understanding of enterprise data and integration patterns, including APIs, event-driven integration, structured and unstructured data, enterprise search, vector databases, data ingestion, storage, retrieval and grounding.
Working knowledge of cloud and application architecture, identity and access management, information security, privacy, responsible AI, observability, reliability, performance, supportability and cost management.
Experience defining and applying AI evaluation methods for groundedness, accuracy, safety, reliability, latency, user experience and operating cost to inform design and production-readiness decisions.
Sufficient hands-on engineering capability to develop targeted prototypes, evaluate implementation approaches and collaborate credibly with engineering teams.
Experience applying process-design methods to rethink workflows, decisions and human-machine interactions around AI capabilities rather than simply automating existing processes.
Exceptional analytical, problem-solving and communication skills, including translating complex AI capabilities, constraints and architecture decisions into clear business terms.
Proven ability to lead complex initiatives through influence and collaborate across business teams, AI Business Transformation, architecture, engineering, data, security, risk and delivery functions.
Demonstrated ability to use AI tools effectively in day-to-day work and critically assess the accuracy, limitations and appropriate use of AI-generated outputs.
Enthusiasm for working in an office and creating a Gold Standard hybrid work culture.

Nice To Haves

Advanced degree or equivalent depth of professional experience.
Proficiency in Python or another modern programming language.

Responsibilities

Partner with the AI Business Transformation Team and business stakeholders to understand prioritized opportunities, operating context, underlying problems, desired outcomes, process constraints and user needs.
Lead discovery and solution-design sessions, challenge assumptions, reframe requirements around intended outcomes, and build stakeholder consensus on recommended approaches.
Translate business outcomes and future-state processes into AI solution concepts, target-state workflows and implementation-ready architectures.
Assess the Firm’s technology landscape and determine whether to use existing enterprise capabilities, configure or extend approved platforms, build custom AI solutions, or pursue non-AI alternatives.
Design secure, scalable AI and agentic architectures that combine probabilistic AI with deterministic rules, workflows and controls, including models, data, grounding, enterprise context, tools, integrations, permissions, human oversight and observability.
Evaluate solution feasibility and tradeoffs across business fit, user experience, data readiness, technical complexity, security, reliability, supportability, performance and cost; provide recommendations to business and technology stakeholders.
Identify high-risk or novel assumptions requiring prototypes, demonstrations or technical spikes; partner with prototyping resources to validate them and develop targeted prototypes when appropriate.
Produce architecture specifications, diagrams, decision records, interface definitions and technical requirements to enable engineering teams to build, test, deploy and support solutions.
Lead engineering handoff and remain engaged through implementation and production release, resolving design questions and ensuring alignment with business outcomes, approved architecture and Firm standards.
Identify gaps in enterprise platforms and reusable capabilities, and recommend standards-aligned services, integrations, controls and architecture patterns for emerging use cases.
Develop and maintain reusable AI patterns, reference architectures, design standards, accelerators, agent skills, harnesses and evaluation methods.
Develop and apply evaluation criteria for AI model and system quality, safety, reliability, performance and cost to guide design decisions and production readiness.
Stay current on generative AI, agentic systems, models, platforms, open standards and architecture practices, and translate relevant developments into practical guidance and recommendations for the Firm.