Description

• Define and drive enterprise architecture strategies, reference architectures, design patterns, and technical standards aligned with business objectives and technology roadmaps.
• Architect scalable, secure, and resilient software platforms, distributed systems, microservices, cloud-native applications, and enterprise integration solutions.
• Lead the design of AI and Generative AI solutions, including LLM integrations, AI agents, RAG pipelines, model orchestration, and intelligent workflow automation.
• Design enterprise data architectures and integration frameworks supporting data ingestion, transformation, quality, governance, APIs, and interoperability across systems.
• Partner with engineering, product, security, and business stakeholders to translate requirements into technical designs while balancing scalability, performance, security, cost, and delivery timelines.
• Establish best practices for software quality, automated testing, CI/CD, observability, fault tolerance, production readiness, and operational reliability.
• Evaluate emerging technologies, cloud services, frameworks, and third-party solutions to guide technology selection, build-versus-buy decisions, and platform modernization.
• Lead architecture reviews, technical assessments, and complex design decisions while identifying risks, performance bottlenecks, and opportunities to reduce technical debt.
• Define security and governance standards covering identity management, data privacy, access controls, responsible AI, and production system reliability.
• Mentor engineers and technical leads, document architectural decisions, and communicate technical strategies and trade-offs to executive and cross-functional stakeholders.

Education and Experience Required

• Associate's degree and a minimum of 9 years' systems analysis and/ or application development work experience or Bachelor's degree and a minimum of 7 years' systems analysis and/ or application development work experience. In lieu of a degree, a combined minimum of 11 years' education and/or relevant work experience, including a minimum of 7 years' systems analysis and/ or application development work experience.
• Expert proficiency in a minimum of 1 relevant programming language and advanced proficiency in a minimum of 1 additional relevant programming language.
• Strong foundation in software engineering principles, data structures, algorithms, and distributed system design.
• Hands-on experience developing production applications using Java, Python, C#, or other modern enterprise programming languages.
• Experience designing and integrating RESTful APIs, microservices, and API-driven architectures.
• Advanced knowledge of Generative AI, Large Language Models (LLMs), prompt engineering, and AI application development.
• Experience building and supporting AI-enabled applications within enterprise SDLC, security, compliance, and governance frameworks.
• Proven ability to influence technical direction, engineering standards, and architectural decisions across multiple teams.

Education and Experience Preferred

• Experience implementing enterprise Generative AI solutions within financial services or other highly regulated industries.
• Deep expertise in Retrieval-Augmented Generation (RAG), embeddings, vector databases, retrieval frameworks, and semantic search technologies.
• Strong understanding of Transformer architectures, attention mechanisms, tokenization strategies, and model evaluation techniques.
• Experience designing AI agents, tool-integrated workflows, and advanced LLM orchestration frameworks.
• Expertise with Microsoft Azure, Azure AI Services, OpenAI technologies, and cloud-native AI platforms.
• Experience establishing enterprise AI governance, responsible AI practices, and model risk management controls.
• Experience leading large-scale technical initiatives, platform modernization efforts, or enterprise capability rollouts.
• Proven experience influencing senior technology and business stakeholders and driving enterprise-wide adoption of new technologies.
• Ability to work autonomously while leading complex technical initiatives across multiple teams.
• Advanced verbal and written communication skills with the ability to present complex technical concepts to executive audiences.
• Proven subject matter expertise in AI platform engineering, software architecture, and enterprise application development.
• Experience with CI/CD pipelines, DevOps tooling, automated testing, observability, and platform reliability engineering practices

Key Skills

Education

Bachelor's degree