AI Solutions, Architecture and Tooling

As 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.

• 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.

• Bachelors 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.