Product Engineer

The Product Engineer at KnowledgeCity is an autonomous, full-lifecycle product delivery owner. Operating within an AI-native model, the Product Engineer owns an assigned product domain end-to-end - from discovery and specification through engineering, quality assurance, deployment, documentation, and continuous improvement - with minimal operational dependency and a high degree of self-sufficiency. Rather than relying on separate specialist teams for execution, the Product Engineer directs a full suite of AI agents across all delivery disciplines, combining product thinking, engineering judgment, and operational accountability to deliver production-ready outcomes at high velocity.

This is not a traditional engineering role. It demands enterprise-grade delivery discipline, production ownership, UX quality accountability, and customer outcome focus - alongside the technical ability to build and ship. The ideal candidate is an autonomous product delivery leader who takes full ownership of their domain, ships frequently, maintains production stability, and drives measurable improvement in the user experience they are responsible for. The Product Engineer reports directly to the Program Delivery Manager.

• →End-to-End Product & Domain Ownership: Own the complete lifecycle of an assigned product domain - discovery, specification, design, engineering, QA, documentation, deployment, and continuous improvement. Full accountability for domain health, user satisfaction, and product quality rests with the Product Engineer.
• →AI Agent Orchestration & Autonomous Execution: Direct and orchestrate a full suite of AI agents to execute across all delivery disciplines. Operate with high autonomy and independent execution, minimizing operational dependency while maintaining complete visibility and control over all agent output.
• →High-Velocity, Quality-First Delivery: Deliver production-ready improvements to your domain on a continuous, high-velocity cadence. Prioritize quality-first delivery over raw output speed - every release must be fully tested, documented, and operationally stable before reaching production. Sustainable engineering practices and delivery consistency are expected.
• →Production Ownership & Post-Deploy Validation: Own all production outcomes for your assigned domain. Conduct post-deployment validation within one hour of every production release - walking through core user workflows, admin journeys, and edge cases on live production data. Monitor deployment health, detect regressions proactively, and initiate rollback immediately if production stability is at risk. You own what ships, and you own what breaks.
• →Delivery & Release Governance: Ensure every release meets enterprise-grade release readiness standards before deployment. Maintain cross-environment testing discipline (development, staging, production). Govern rollback readiness for every release. Track deployment quality metrics across releases and take corrective action when stability trends degrade. Customer-facing workflows are always treated as critical paths.
• →UX Quality & Customer Workflow Ownership: Own the UX quality of your domain as rigorously as its technical correctness. Evaluate customer workflows critically - not just for functional correctness, but for usability, clarity, and operational readiness. Commission regular UX audits using AI UX agents. Validate that every stakeholder-facing feature is intuitive, complete, and ready for real users before it ships.
• →Documentation & Knowledge Management: Ensure complete, accurate documentation ships simultaneously with every feature - user guides, API documentation, and operational notes. Documentation is not an afterthought; it is part of the definition of done. No feature is production-ready without it.
• →Stakeholder Communication, Escalation & Transparency: Communicate proactively with your Program Delivery Manager or Head of Product - without waiting to be asked. Surface delivery risks, dependency blockers, and scope changes early. Provide clear delivery forecasts, release coordination updates, and post-deployment status. Escalate issues with proposed solutions, not raw problems. Operational transparency is a professional standard, not an optional practice.
• →Product Thinking & Business Impact Ownership: Approach every delivery decision through a product ownership lens. Understand the business impact of what you build, prioritize improvements by customer outcome rather than technical convenience, and apply product lifecycle thinking to your domain. Connect your technical delivery to measurable user satisfaction signals, support ticket reduction, and feature adoption metrics.
• →Continuous Improvement & Backlog Ownership: Maintain a prioritized improvement backlog for your domain. Continuously source gap intelligence from support tickets, QA audits, UX findings, usage analytics, and stakeholder feedback. Apply the opportunity scoring framework to prioritize by user impact, business value, and implementation effort.

• 5+ years of hands-on experience in product management, engineering management, or a closely related discipline - with demonstrated ability to own and deliver end-to-end product outcomes independently.
• Proven track record of shipping real, production-grade products end-to-end - not just features within a larger team structure.
• Background spanning at least two of: software engineering, product management, UX design, or QA - with the ability to operate credibly across the full product lifecycle.
• Hands-on experience with AI-assisted development tools and a strong understanding of how to direct and review AI-generated output at production quality.
• Demonstrated experience owning production outcomes - including post-deploy validation, regression management, incident response, and deployment health monitoring.
• Experience evaluating and improving customer-facing workflows and UX quality - not limited to technical implementation alone.
• Background in agile, high-velocity delivery environments with a strong bias for continuous, quality-first delivery rather than batch-release cycles.
• Familiarity with database performance fundamentals (query optimization, indexing, execution planning) is a strong plus.
• EdTech or SaaS product experience preferred but not required.
• Bachelor's degree in Computer Science, Software Engineering, or a related technical discipline required. Master's degree preferred.

• Full product lifecycle ownership: strategy, discovery, design, engineering, QA, documentation, deployment, and post-release validation - independently and at pace.
• Production accountability mindset: you own what ships, you own what breaks, and you fix it - without escalating raw problems or waiting for direction.
• Customer workflow thinking: ability to evaluate product experiences from a real user's perspective, not just a technical implementation standpoint.
• Enterprise delivery discipline: release governance, cross-environment testing, rollback readiness, deployment quality tracking, and stability assurance.
• AI agent direction: operates and orchestrates AI agents as high-output delivery partners - not as a crutch. Reviews every output critically before it touches production.
• Product intuition: strong instinct for what to build, why it matters to users, and how to connect engineering decisions to business outcomes.
• Proactive communication: surfaces risks and blockers early, communicates delivery status clearly, and escalates with solutions - not questions.
• Systems thinking: understands how their domain connects to the broader product ecosystem and considers upstream and downstream impact before making changes.
• Self-managing execution: operates with minimal supervision, unblocks independently, and holds a consistently high quality bar without external enforcement.
• Advanced English proficiency, both written and spoken, is mandatory for effective communication with partners and clients.