Senior AI Architect Finance Technology AI Enablement About the Role We are looking for a Senior AI Architect who will serve as the top AI/ML technical authority for the finance IT organization someone who can translate a business transformation vision into a coherent AI/ML technical strategy, design the end-to-end architecture that makes that strategy real, and guide engineering teams through detailed design and implementation. This is a highly senior, hands-on architecture role. You will operate at multiple altitudes: shaping multi-year AI/ML roadmaps with business and technology leaders, producing detailed reference architectures and design standards, and working directly with engineers to solve hard design problems. You will be the primary architectural voice for GenAI, machine learning, automation, and agentic systems across finance technology. What You'll Do Strategy & Vision Define the AI/ML technical strategy that underpins the finance organization's broader business transformation vision. Partner with business leaders and senior stakeholders to translate financial and operational goals into actionable AI/ML and automation initiatives. Establish and evangelize an AI-first architectural philosophy across finance technology identifying where GenAI, ML, and automation should be embedded by design rather than bolted on. Act as a trusted advisor to business and technology executives on AI/ML capability, feasibility, risk, and value. Architecture & Roadmap Create high-level automation and AI/ML architecture designs and multi-year technology roadmaps aligned to business priorities. Architect AI/ML foundational frameworks and platforms that can be reused across multiple finance use cases (fraud detection, forecasting, reconciliation, reporting, analytics, Intelligent document/content/Audio Processing and beyond). Design high-performing, complex semantic layers using advanced techniques such as RAG, Knowledge Graphs, and GraphRAG, enabling natural language query against large, complex financial databases with extensive table relationships. Define reference architectures for agentic AI workflows, including multi-agent orchestration, tool use, memory, and human-in-the-loop patterns for finance process automation. Set architecture and design standards, frameworks, and best practices for AI/ML solution delivery across the organization. Detailed Design & Engineering Guidance Produce detailed architecture designs data flows, integration patterns, model serving, event-driven pipelines, security and governance controls that engineering teams can build against. Guide and mentor engineers through detailed technical design decisions, code/design reviews, and solution build-out. Own architectural quality, scalability, and performance for high-volume financial data processing and analytics pipelines. Evaluate and select tools, platforms, and frameworks (cloud-native and third-party) to support GenAI, ML, and automation initiatives. Delivery & Governance Ensure solutions meet enterprise, regulatory, and financial-data governance requirements (data privacy, model risk, auditability, explainability). Drive adoption of MLOps/LLMOps practices for model lifecycle management, monitoring, and continuous improvement. Represent AI/ML architecture in design authority / architecture review forums and champion consistent standards across teams. What You'll Bring Experience 12+ years of progressive experience in enterprise architecture, cloud architecture, and distributed systems/platform solutions. Proven hands-on experience across major cloud platforms Azure, Google Cloud Platform, and AWS. Deep experience with Kubernetes and container-based platform architecture. Strong background in event-driven architectures/frameworks and high-volume, high-throughput data processing systems. Demonstrated experience architecting GenAI and Machine Learning solutions in production environments. Experience in financial data processing and analytics, ideally within a finance, banking, or FinTech IT organization. Track record architecting AI/ML foundational frameworks/platforms used across multiple teams or use cases. Technical Depth GenAI: LLM application architecture, prompt/context engineering, RAG, Knowledge Graphs, GraphRAG, semantic search, vector databases. Agentic AI: designing and architecting complex multi-step, multi-agent agentic workflows and orchestration patterns. Machine Learning: forecasting, fraud detection, anomaly detection, and predictive modeling architectures. Automation: business process automation and data processing automation architecture, including intelligent/AI-augmented automation. Analytics AI: architecture for AI-enabled analytics and NLP-based querying against large, complex relational data models. Data & Integration: large-scale data pipelines, complex database schemas, semantic layer design, API and event-driven integration patterns. Cloud & Platform: Azure and Google Cloud Platform AI/ML services (e.g., Azure OpenAI, Azure ML, Vertex AI, BigQuery ML), Kubernetes-based deployment, scalable microservices. Leadership & Soft Skills Ability to operate credibly at both strategic (executive/business stakeholder) and detailed (engineering) levels. Strong stakeholder management and communication skills; able to influence business leaders and technical teams alike. Experience setting technical standards, design principles, and best practices at an organizational level. Strong mentoring and technical leadership skills comfortable guiding engineers through detailed design and build. Structured, first-principles thinker who can bring architectural rigor to ambiguous, transformation-scale problems. Nice to Have Prior experience within a Finance Technology, Banking, or regulated financial services environment. Experience with MLOps/LLMOps tooling and model governance/risk frameworks. Relevant cloud or architecture certifications (Azure Solutions Architect, Google Cloud Platform Professional Cloud Architect, AWS Solutions Architect, TOGAF, etc.).
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