Role DescriptionAs the Director of AI Solutions, Architecture and Tooling in the Platform Engineering team, you will define and lead the enterprise approach for designing, building, and scaling AI/GenAI solutions. You will own reference architectures, engineering patterns, and the approved developer toolchain that enable teams to deliver secure, reusable, production-grade AI capabilities on Databricks and Azure. You will partner with architecture, technology, data, cybersecurity, risk, and business leaders to translate business needs into practical solution designs and platform roadmaps.
This is a hands-on technical leadership role that combines architecture ownership with engineering enablement. You will establish the standards and reusable assets that guide AI solution delivery, lead complex design decisions, evaluate the evolving tool ecosystem, and build a team that accelerates adoption while maintaining reliability, governance, and cost discipline in a regulated financial environment.
Role Objectives- Define the enterprise AI solution architecture strategy, reference architectures, engineering standards, and guardrails for secure and scalable AI/GenAI delivery on Databricks and Azure Cloud Services.
- Own the AI developer toolchain and golden paths, including reusable SDKs, templates, development environments, and patterns that improve engineering speed, consistency, and compliance.
- Lead solution architecture for retrieval-augmented generation, agentic workflows, document intelligence, model integration, multimodal AI, and other enterprise use cases.
- Define and guide reusable platform services and integration patterns for identity, data access, model access, observability, prompt management, and downstream application consumption.
- Establish and lead architecture reviews and the enterprise path-to-production, partnering with cybersecurity, risk, data governance, and responsible-AI stakeholders to embed required controls.
- Evaluate AI models, frameworks, vendors, and engineering tools through structured proofs of technology, and make clear recommendations for adoption, standardization, or retirement.
- Drive engineering quality through standards for APIs, testing, CI/CD, infrastructure-as-code, performance, resilience, observability, and cost-efficient solution design.
- Partner with AI Capability Development and AI-Ops leaders to move reference designs into reusable capabilities and reliable production services with clear ownership and operating models.
- Build, mentor, and lead a team of architects and senior engineers while influencing engineering practices and technical decisions across the broader organization.
Qualifications and Skills- Bachelor's degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field; an advanced degree is a plus.
- 8+ years of hands-on experience in AI/ML engineering, software platform engineering, or enterprise application architecture, including 3+ years in a technical leadership, architect, or engineering lead capacity.
- Demonstrated experience defining enterprise-scale AI/GenAI architectures, reference patterns, technical standards, and roadmaps across multiple teams or business domains.
- Advanced Python skills and deep experience with AI/GenAI frameworks and services such as Databricks Vector Search, Azure AI Search,
- Azure AI Document Intelligence, LangGraph, Haystack, or LlamaIndex.
- Deep knowledge of retrieval-augmented generation, agentic architectures, prompt engineering, embedding models, vector databases, model integration, and evaluation patterns.
- Strong hands-on expertise with Databricks, Azure cloud services, distributed data platforms, and enterprise integration patterns.
- Demonstrated experience developing RESTful and event-driven services, microservices, containerized applications, CI/CD systems, and infrastructure-as-code.
- Experience building developer platforms, approved toolchains, golden paths, or reusable engineering frameworks that improve adoption across distributed teams.
- Familiarity with AI governance, responsible AI, cybersecurity, privacy, and control requirements in a regulated environment.
- Proven ability to build and lead technical teams, communicate with executives, and influence senior technical and non-technical stakeholders across organizational boundaries.
SMBC's employees participate in a Hybrid workforce model that provides employees with an opportunity to work from home, as well as, from an SMBC office. SMBC requires that employees live within a reasonable commuting distance of their office location. Prospective candidates will learn more about their specific hybrid work schedule during their interview process. Hybrid work may not be permitted for certain roles, including, for example, certain FINRA-registered roles for which in-office attendance for the entire workweek is required.
