AI PDLC Architect

Candescent is a forward-thinking technology company transforming how financial institutions deliver Intelligent Banking experiences. We unite digital banking, account opening, and branch solutions that power and connect digital banking, account opening, and branch solutions—creating seamless engagement across digital, remote, and in-person channels.

Our Experience-Led, Intelligence-Driven approach combines human-centered design with data, automation, and cloud-based innovation. Built on an API-first architecture, our extensible ecosystem enables institutions to adapt quickly, integrate easily, and unlock new opportunities for growth—turning every customer interaction into a moment of clarity, confidence, and connection.

The AI-PDLC Architect is responsible for evolving Candescent's Product Development Lifecycle (PDLC) from traditional human-centric workflows to AI-augmented and AI-native ways of working.

This role partners across Product, Engineering, Agile Coaching, Enablement, and Transformation teams to identify, evaluate, standardize, and scale AI-enabled practices that improve product delivery, learning, quality, and operational effectiveness.

Acting as a strategic thought leader and practitioner, the AI-PDLC Architect develops reusable standards, maturity models, coaching frameworks, and architecture recommendations that enable responsible AI adoption across the Product organization. This role does not own AI products or enterprise AI platforms but serves as the authority on how AI transforms the way product development work is performed.

Key Responsibilities and Deliverables AI Practice Discovery & Standards Development

• Discover, evaluate, and document AI usage patterns across product strategy, discovery, design, engineering, testing, release, and continuous improvement activities.
• Develop and maintain an AI Practice Catalog, AI Tool Registry, and AI-PDLC standards repository.
• Convert successful AI experiments into repeatable, scalable standards, frameworks, and operating practices.

PDLC Architecture Evolution

• Define and evolve AI-enabled Product Development Lifecycle practices across Product Success.
• Evaluate opportunities to transition work from human-led to AI-assisted or AI-native operating models.
• Assess dependencies, governance impacts, workflows, and organizational implications of AI adoption across the PDLC.

AI Adoption & Organizational Enablement

• Create maturity models, adoption frameworks, coaching materials, and leadership guidance to support AI-enabled ways of working.
• Partner with Product Areas, Product Groups, Agile Coaches, and Product leaders to drive responsible adoption.
• Facilitate cross-functional alignment on AI-PDLC priorities and standards.

AI Tool Evaluation & Recommendations

• Establish criteria for evaluating AI tools across usability, workflow impact, security, integration, scalability, cost, and governance.
• Recommend adoption, pilot, hold, or retirement decisions based on evidence and organizational value.
• Partner with Enterprise Architecture, Security, Legal, and Risk teams to ensure compliance and responsible implementation.

Organizational Learning & Continuous Improvement

• Build mechanisms to capture lessons learned, successful practices, adoption barriers, and emerging risks.
• Create executive-level reporting and intelligence that informs future AI investments and operating model decisions.
• Enable visibility across Product Areas to reduce duplication and accelerate organizational learning.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, Product Management, Business, or related field.
• 10+ years of experience in Product Management, Product Operations, Agile Transformation, Engineering Leadership, Product Development, or related disciplines.
• 5+ years of experience driving Agile, Lean, Product Operating Model, or Digital Transformation initiatives across multiple teams or business units.
• 3+ years of experience applying AI, Generative AI, Machine Learning, or AI-enabled productivity practices within product development or technology organizations.
• Deep expertise across the end-to-end Product Development Lifecycle, including product strategy, discovery, requirements, delivery, testing, release management, measurement, and continuous improvement.
• Experience developing operating models, frameworks, governance mechanisms, standards, or enterprise-wide best practices.
• Demonstrated success influencing senior leaders and driving change without direct authority.
• Strong facilitation, coaching, organizational consulting, and stakeholder management skills.
• Experience evaluating emerging technologies and recommending enterprise adoption strategies.

Knowledge, Skills & Competencies

• Deep understanding of Agile methodologies, Scrum, Kanban, Lean product development, and flow-based delivery models.
• Strong systems-thinking capabilities with the ability to assess organizational impacts across people, processes, technology, governance, and operating models.
• Applied knowledge of…