The AI Architect defines and guides scalable, maintainable, AI-enabled product architecture for modern product delivery and legacy modernization. This role translates product goals, customer commitments, engineering constraints, and modernization needs into practical architecture, service models, integration patterns, technical standards, and reusable solution accelerators. The role is expected to use AI tools to improve architecture discovery, documentation, design comparison, codebase understanding, modernization planning, and engineering enablement while maintaining accountability for technical decisions. 

Key Outcomes 

• Clear architecture direction for new product features, modernization, cloud adoption, integrations, and scalable product capabilities. 

• Reduced technical debt and increased maintainability through service modeling, architecture standards, reusable patterns, and modernization roadmaps. 

• Improved engineering productivity through AI-assisted architecture documentation, codebase analysis, design accelerators, and internal architecture bots. 

• Stronger alignment among product, engineering, QA, DevOps, data, and leadership stakeholders around feasible and sustainable technical solutions. 

Roles and Responsibilities 

• Lead product architecture for new capabilities, modernization initiatives, service decomposition, API strategies, integration patterns, data flows, cloud adoption, and non-functional requirements. 

• Use GitHub Copilot, ChatGPT Enterprise/Team, and approved AI tools to accelerate codebase exploration, design option comparison, architecture documentation, sequence diagrams, ADR, risk analysis, and modernization planning. 

• Develop and maintain conceptual, logical, and physical service models that support product initiatives, integration requirements, operational needs, and long-term maintainability. 

• Define architecture standards, design patterns, API guidelines, service boundaries, data handling patterns, observability expectations, and quality attributes for product teams. 

• Analyze current-state architecture, product performance, reliability, scalability, technical debt, integration constraints, and modernization opportunities. 

• Partner with product management to translate business priorities and customer commitments into technically feasible roadmaps, implementation strategies, and release increments. 

• Guide engineers and QA teams on design decisions, testability, service contracts, performance risks, backward compatibility, and modernization safety. 

• Create proofs of concept to evaluate feasibility, desirability, viability, and operational impact of emerging technologies, cloud capabilities, AI-enabled engineering accelerators, and internal bots. 

• Establish product-level architecture governance that is lightweight, practical, and aligned to agile delivery 

• Mentor senior engineers and engineering managers in system design, service ownership, technical debt management, AI-assisted architecture practices, and full-cycle engineering standards. 

• Document architecture decisions, diagrams, service catalogs, integration patterns, technical risks, and modernization roadmaps in formats usable by engineers and product stakeholders. 

• Continuously review industry trends and internal product constraints to evolve architecture practices, technology direction, and AI-enabled engineering adoption. 

Education 

• Bachelor’s degree in Computer Science, Information Technology, Software Engineering, Mathematics, or a related discipline is required; equivalent deep technical experience may be considered. 

• Master’s degree in Computer Science, Software Engineering, Data Engineering, Architecture, or related discipline is preferred. 

• Cloud, architecture, DevOps, data, or AI-related certifications are preferred when supported by practical implementation experience. 

Experience 

• Overall Experience of 14+ years in software development and architecture. 

• 8+ years of software development, product engineering, architecture, or technical design experience. 

• 5+ years of senior technical leadership, architecture ownership, product modernization, service design, integration architecture, or platform engineering experience is preferred. 

• Proven experience designing scalable, secure, maintainable, high-performance product architectures for complex applications. 

• Experience with legacy modernization, cloud services, distributed systems, API design, data architecture, technical debt management, and cross-functional solutioning. 

• Experience using AI tools for architecture analysis, documentation, codebase understanding, proof-of-concept acceleration, or engineering enablement is preferred. 

Skills 

• Expert knowledge of software architecture, system design, service modeling, integration patterns, cloud-native design, distributed systems, and non-functional requirements. 

• Strong understanding of full-stack product engineering, databases, APIs, event-driven systems, CI/CD, observability, performance, reliability, and security principles. 

• Ability to use AI tools to analyze legacy systems, summarize codebase behavior, compare design alternatives, identify risks, draft documentation, and accelerate proof-of-concept work. 

• Ability to create architecture decision records, diagrams, service catalogs, modernization roadmaps, technical standards, and reusable engineering patterns. 

• Strong communication skills with the ability to align technical and non-technical stakeholders around architecture decisions and trade-offs. 

Abilities 

• Influences across engineering, product, QA, DevOps, data, and leadership teams without relying only on formal authority. 

• Balances long-term architecture health with customer commitments and delivery constraints. 

• Converts ambiguity into clear architecture direction, incremental modernization plans, and executable engineering guidance. 

• Mentors teams toward full-cycle ownership, reusable patterns, and accountable AI-enabled engineering practices. 

• Maintains practical governance that improves quality and speed rather than increasing unnecessary process overhead. 

Tools and Technologies 

• GitHub Copilot, ChatGPT Enterprise/Team, Claude, approved AI assistants for architecture exploration, documentation, code explanation, modernization analysis, proof-of-concept acceleration, and engineering enablement. 

• Architecture and documentation tools for diagrams, ADRs, service catalogs, backlog alignment, and architecture reviews. 

• C#, .NET/.NET Core, NET Framework, Java, JavaScript, TypeScript, Python, SQL, APIs, microservices, Angular/React, Node.js, and legacy technologies relevant to existing products. 

• AWS services including Lambda, ECS, S3, IAM, Cognito, Step Functions, SQS, CloudWatch, Aurora, Glue; Azure services where applicable. 

• Kafka/streaming, relational and NoSQL databases, PostgreSQL, MongoDB, DynamoDB, Elasticsearch, data lakes, distributed computing tools where applicable. 

• CI/CD tools such as Jenkins, Terraform, Docker, Git/Gerrit/GitHub, NuGet, NPM, Yarn, and feature management tools. 

• Hands-on experience with automation frameworks and tools, including Reqnroll, BDD, C#, .NET, Appium, and Selenium. 

AI Usage and Accountability Requirements 

• AI-assisted architecture recommendations must be validated against product context, engineering constraints, performance needs, maintainability, and operational risk. 

• Architecture decisions must remain traceable to business goals, quality attributes, implementation feasibility, and known trade-offs. 

• AI tools should accelerate discovery and documentation but not replace architecture review, stakeholder alignment, or hands-on technical validation. 

• Reusable AI prompts, architecture bots, documentation patterns, and design-review checklists should be maintained as organizational assets. 

Success Measures / Accountable Outcomes 

• Architecture adoption rate, reusability of patterns, reduction in duplicated designs, and clarity of architecture documentation. 

• Reduction in technical debt, integration complexity, modernization risk, and repeated architecture defects. 

• Improvement in delivery readiness for strategic product initiatives and customer-committed capabilities. 

• Engineering productivity gains from reusable architecture assets, prompts, bots, templates, and proof-of-concept accelerators. 

AI Competency Alignment