Director of Software Engineering & Applied AI (Level 6) – Product Success Engineering, Adobe Experience Platform

Adobe•San Jose, CA

About The Position

The Adobe Experience Platform (AEP) Product Success Engineering (PSE) team is seeking a n innovative Director of Software Engineering & Applied AI to lead the development of AI agents, tool ing, and automation to help customer s directly support , operate, and scale their end-to-end AEP and Apps implementation. Our team seeks to revolutionize customer support & success through intelligent automation and AI agents across the AEP portfolio . You r work will help fundamentally transform how we serve , support, and accelerate time-to-value for our customers by empowering them directly with engineering best practices. A s the engineering leader , you will be responsible for the design and develop ment of Agentic-first solutions that span across core customer success domains: Troubleshooting & Support - Solve AEP customer support issues through intelligent diagnosis, deflection, and resolution systems that leverage AI agents for automated issue diagnosis, intelligent ticket creation/routing, and self-service troubleshooting capabilities Run & Operate - Proactively monitor, remediate, and scale AEP in adherence to implementation best practices to prevent support issues through automated remediation workflows, performance optimization agents, and predictive scaling mechanisms that maintain AEP customer-specific implementation reliability Adoption & Value - Advise, accelerate, and grow AEP customer adoption and value realization through customer journey intelligence systems, use case & feature adoption recommendations, and usage pattern analysis tools In this role, you will collaborate with other members of our multidisciplinary team , including solution architects and data sci entists, to build scalable systems that transform and establish our commitment to setting new industry benchmarks for product success engineering while driving measurable business impact and improved customer outcomes.

Requirements

Bachelor's degree in Computer Science , Engineering, or related technical field, with 10+ years of software engineering experience , including 5+ years in engineering leadership roles managing teams of 10+ engineers
Demonstrated expertise in Applied AI/ML with 3-5 years of hands-on experience deploying production AI systems, including deep understanding of LLMs, generative AI, prompt engineering, and RAG architectures
Proven track record of architecting and delivering large-scale, cloud-native platforms and distributed systems, with expertise in microservices, API design, and enterprise integration patterns
Strong background in customer intelligence platforms, analytics systems, and data pipeline architecture, with experience building real-time decision engines and predictive modeling systems
Product and growth mindset, with a strong ability to translate customer challenges into scalable, automated solutions that drive adoption and business impact
Deep understanding of B2B SaaS customer success methodologies, including customer health scoring, churn prediction, adoption analytics, and expansion revenue optimization
Experience leading customer-facing engineering teams or technical account management organizations, with demonstrated ability to translate customer feedback into product improvements
Exceptional communication skills with the ability to influence senior stakeholders, present to executive leadership, and drive alignment across diverse technical and business teams
Proven ability to hire, develop, and retain top engineering talent while fostering inclusive, high-performance team cultures focused on customer outcomes

Nice To Haves

Experience embedding self-service BI , AI-powered recommendation s , and workflow automation into enterprise SaaS products
Knowledge of customer engagement strategies, marketing automation, and digital adoption platforms
E xpertise in Adobe Experience Platform (AEP) and/or Adobe Experience Platform Developer c ertifications a plus

Responsibilities

Strategic Leadership & Vision Define and execute the technical strategy for AI-powered customer success solutions across Adobe's AEP portfolio, aligning engineering initiatives with business objectives and customer outcomes Lead the design and development of agentic AI systems that autonomously diagnose, prevent, and resolve customer issues while accelerating adoption and value realization
Team Management & Organizational Development Build, mentor, and scale a world-class multidisciplinary engineering team of 15-25+ engineers, data scientists, and solution architects, fostering a culture of innovation, customer obsession, and technical excellence Collaborate effectively across multiple vertically aligned engineering teams, product management, UX design, and field organizations to ensure seamless integration of customer success solutions into AEP
Product & Technical Execution Oversee the end-to-end development lifecycle of intelligent automation systems, AI agents, and customer intelligence platforms that transform reactive support into proactive, data-driven customer success operations Drive the implementation of customer-facing analytics, predictive insights, and recommendation engines embedded directly into AEP's user experience to enable self-service optimization
Customer Success Engineering Innovation Champion the development of scalable automation solutions targeting 85%+ first contact resolution rates, 99.9%+ platform uptime, and measurable customer health score improvements Establish engineering best practices for AI model deployment, system reliability, and operational excellence while ensuring compliance with security, privacy, and regulatory standards
Cross-Functional Partnership & Influence Serve as the technical voice of customer success engineering in executive forums, translating complex technical concepts into business impact and strategic initiatives Partner closely with Forward Deployment Engineers, Field Support Consultants, and Customer Success Managers to understand customer challenges and translate them into scalable engineering solutions