• Define and maintain the enterprise AI architecture blueprint and reference models. • Evaluate and select AI platforms, frameworks, vector databases, LLMs, and tooling for application integration. • Provide architectural leadership for generative AI and agentic workflows in products and internal applications. • Establish patterns for retrieval augmented generation (RAG), model orchestration, and evaluation pipelines. • Partner with Data & Analytics to design end to end data flows that support AI workloads, including ingestion, transformation, storage, and retrieval patterns that ensure accuracy and performance. • Ensure AI solutions integrate cleanly with enterprise data ecosystems by defining standards for metadata, lineage, governance, and interoperability across operational systems, data pipelines, and analytical platforms. • Lead end-to-end architecture for AI powered features, including model integration, API design, data flows, and security controls. • Work with development teams to ensure AI components are modular, scalable, and resilient. • Guide teams in fine tuning, prompt engineering, model optimization, and inference best practices. • Oversee architectural reviews and provide hands-on technical support during implementation. • Partner with Security and Compliance teams to ensure AI systems follow responsible AI principles and risk controls. • Define processes for model monitoring, safety evaluations, versioning, data lineage, and auditability. • Ensure adherence to data privacy, intellectual property, and regulatory standards. • Advise the CIO, CTO, and senior leadership on emerging AI technologies and strategic opportunities. • Mentor developers and technical leads to build organizational capability in AI engineering. • Work cross-functionally with product management to translate business needs into AI architectural patterns. • Represent the Application Development team in AI governance and enterprise architecture forums. • Interface and knowledge share with domain experts in Research and Advisory groups.

• Build LLM-powered systems (RAG, agents, copilots, automation tools) • Design data pipelines for structured and unstructured data (PDFs, APIs, databases) • Implement retrieval systems (vector DBs, embeddings, semantic search) • Integrate and evaluate models (OpenAI, Claude, Gemini, open-source) • Ship APIs and microservices (FastAPI / Flask / Node) • Implement prompt engineering, evaluation, and guardrails • Deploy on cloud (AWS / GCP / Azure / IBM) • Monitor accuracy, latency, cost, and failure modes • Work directly with stakeholders to turn fuzzy needs into shipped systems

• Define and drive the vision for a modern AI platform that serves as the foundation for product innovation across the company. • Own a portfolio of high-impact capabilities—semantic search, AI guardrails, agentic frameworks, and similarity and recommendation models—that teams rely on to deliver customer value. • Build for builders: design APIs, abstractions, and developer experiences that make it easy for product teams to integrate sophisticated AI into their solutions. • Establish guardrails and governance frameworks that ensure our AI capabilities are safe, reliable, and aligned with responsible AI principles. • Navigate the rapidly evolving AI landscape, evaluating build-vs-buy decisions and staying ahead of emerging technologies and best practices. • Partner closely with engineering, applied science, and design teams to lead discovery, definition, and delivery of platform capabilities that drive measurable business outcomes.

• Collaborate with technology experts • Develop a state of the art financial business platform • Build a scalable solution using Amazon Web Services • Develop skills coding, testing, and evaluating technical tradeoffs