Warren Reyes

Led end-to-end delivery of AI-enabled product initiatives, owning requirements-to-release execution across workflow planning, implementation-ready specifications, validation, and continuous improvement. Converted business needs into functional and technical requirements, user stories, acceptance criteria, and AI decision logic for consistent AI behavior in real scenarios. Served as an AI logic owner by reviewing workflow behavior, improving response quality, strengthening edge-case handling, and defining expected outcomes. Coordinated product, engineering, QA, and business stakeholders across concurrent initiatives, managing dependencies, scope decisions, and release expectations. Established stronger QA and validation practices for AI functionality through clearer test checkpoints, triage expectations, and release-readiness reviews, improving release stability and reducing downstream rework. Standardized planning artifacts, decision tracking, testing checkpoints, and release communica

Managed software and intelligent automation initiatives with distributed engineering teams and multiple business stakeholders under evolving requirements. Translated business objectives into technical documentation, delivery plans, backlog priorities, workflow definitions, and implementation-ready requirements to reduce ambiguity before development began. Led Agile delivery activities including sprint planning, scope alignment, backlog coordination, dependency management, issue escalation, and milestone tracking. Partnered with QA to improve validation coverage, testing discipline, defect prioritization, and release stability. Helped shape early AI-enhanced workflow capabilities by defining logic flows, use cases, expected system behavior, operational review steps, and handoff criteria. Improved cross-functional communication with structured updates, decision documentation, risk visibility, and ownership of open actions.

Supported development and rollout of AI-assisted and workflow automation solutions for enterprise and internal environments. Gathered stakeholder needs and translated them into functional requirements, technical specifications, implementation tasks, workflow rules, and expected system outcomes. Worked with engineering and QA teams to define system behavior, logic rules, test expectations, and quality benchmarks for AI and automation features. Coordinated feature readiness, test planning, issue triage, defect follow-up, and release support to improve delivery consistency. Reduced ambiguity in handoffs by clarifying requirements, documenting workflow expectations, and ensuring implementation teams had clear acceptance conditions. Contributed to post-launch reviews, feature documentation, and process improvements based on delivery findings and stakeholder feedback.