Product/BA Lead + Developer (AI-Augmented)

As the Product/Business Analysis Lead and Developer, you will act as the bridge between stakeholders and technology. You will capture institutional requirements, design business processes, and transform them into working ERP/LMS/SIS applications. Leveraging AI coding assistants, AI Scrum Master tools, and automated QA, you will deliver solutions with the speed and efficiency of a larger team. Key Responsibilities Business Analysis & Product Ownership Engage with clients and stakeholders to understand needs and translate them into clear requirements. Document and refine business processes in education management and HR (admissions, SIS, LMS, reskilling, internships, alternance). Use AI tools to generate user stories, acceptance criteria, and initial process models. Prioritize backlog items and align releases with institutional objectives. Software Development & Delivery Design and develop ERP/LMS/SIS modules, initially on ERPNext and progressively in a hybrid ERPNext + microservices stack. Use AI-powered IDEs (Cursor, Copilot) to generate, refactor, and debug code efficiently. Build and maintain APIs for integration with ministry dashboards and external systems. Implement modern frontends (React/Next.js, Tailwind) using AI UI builders (Vercel v0, DhiWise, Anima). Deploy applications on cloud infrastructure (OVH VPS, later Kubernetes/Supabase/Convex). AI-Augmented Workflows Collaborate with an AI Scrum Master for sprint planning, task tracking, and reporting. Rely on automated testing agents to generate unit/integration tests and ensure reliability. Use AI DevOps assistants for infrastructure-as-code, deployment pipelines, and monitoring. Continuously evaluate and integrate emerging AI development tools to maximize productivity. Governance & Quality Ensure compliance with data protection and cybersecurity requirements, especially for ministry-scale systems. Document decisions and maintain system knowledge (AI-assisted documentation, human validation). Drive adoption of best practices in AI-human collaboration, ensuring outputs are efficient and maintainable.