Principal Architect

Role Summary
The Principal Architect is a senior technical leader responsible for defining and driving the architecture of enterprise-grade software, data, and AI solutions. This role combines deep software engineering expertise, enterprise architecture capabilities, and strategic technical leadership to deliver scalable, secure, resilient, and high-performance platforms.
The Principal Architect will guide architectural decisions across AI-enabled applications, data platforms, cloud-native systems, and enterprise integrations. Working closely with engineering, product, and business leaders, this role translates business objectives into practical technical solutions, establishes engineering standards, and supports the delivery of innovative products and platforms.

Primary Responsibilities
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.