Manoj Khatri

• Architected and delivered an end-to-end data ingestion and reconciliation system supporting CSV/Excel uploads, real-time preview, and intelligent fuzzy matching; built scalable APIs with Django REST Framework and an interactive UI using React to streamline user-driven validation and approval workflows.
• Integrated automated ticket generation with the QuickXtract platform, implementing robust data validation, normalization, and error-handling pipelines to improve data accuracy, reduce manual effort, and ensure reliable processing at scale.
• Developed an Invites Management module enabling users to track invite status (sent, pending, accepted) through a real-time dashboard, improving visibility and control over user onboarding workflows. Built interactive UI using React and integrated with backend services for seamless data synchronization.
• Designed and implemented a backend invitation system using Django to handle email-based invites, including customizable email templates, automated delivery workflows, and status tracking to enhance user engagement and onboarding efficiency.
• Enhanced authentication backend for LDAP users by modifying login flow to make service account validation optional, improving flexibility in enterprise authentication scenarios while maintaining secure access control within the system. Built and maintained the solution using Django.
• Integrated Google Gemini API to implement OCR functionality for PDF document processing, enabling automatic extraction of structured text from uploaded files and improving data digitization accuracy and workflow efficiency.

• Led multiple phases of the Software Development Life Cycle (SDLC),
delivering scalable backend systems using Python with Django, while
leveraging enterprise Java ecosystems with Spring Boot for high-performance
backend services.
• Enhanced system intelligence and data processing workflows by
incorporating AI-assisted features such as document processing and
structured data extraction, aligning backend systems with emerging AI-driven
automation capabilities.
• Integrated AI-powered OCR and data extraction capabilities into backend
workflows using external AI services, enabling automated processing of
unstructured documents and improving operational efficiency and data
accuracy.
• Architected and deployed microservices-based systems on AWS using Docker
and Kubernetes, improving system reliability, scalability, and resilience under
high load environments.
• Implemented AI-adjacent enhancements in backend workflows, including
automated decision support logic, structured data validation, and intelligent
processing pipelines to reduce manual effort and improve system accuracy.
• Improved application performance and scalability by optimizing backend
architecture and database queries across Python and Java systems,
leveraging Hibernate and relational databases for efficient data handling.