Avni Singh

Software Engineer with 1+ year of industry experience building AI-powered software and production-grade backend systems. Experienced in developing LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and agentic AI systems, alongside scalable REST APIs and full-stack applications using modern software engineering practices. Strong foundation in Python, FastAPI, distributed systems, and integrating AI capabilities into production workflows.

AI/ML Intern at Bharat Forge (2025-07 – Present)

Fleet Management System and Warehouse Management System development

• Designed and developed backend services and REST APIs for a production fleet management platform, enabling mission creation, task dispatch, lifecycle management, and real-time communication between warehouse applications and autonomous systems
• Built graph-based task scheduling and conflict management modules with dynamic re-routing, while implementing request validation, structured logging, and persistent mission state using MongoDB, MQTT, and Express
• Developed real-time operational dashboards for fleet health, mission execution, and dispatch analytics using React, TanStack Query, and Recharts, providing production teams with live system visibility and performance insights
• Developed a full-stack warehouse workflow platform enabling QR-based task creation, automated transport dispatch, and end-to-end task lifecycle management using React, Node.js, Express, and PostgreSQL
• Designed and implemented backend APIs integrating the Warehouse Management System with the Fleet Management System, enabling reliable task dispatch, callback-based status synchronization, and consistent workflow tracking across distributed services
• Built transport monitoring dashboards with live task status, robot statistics, historical snapshots, and system health monitoring, improving operational visibility through persistent event logging and real-time data synchronization

Research Intern at DRDO (Defence Research and Development Organisation) (2024-12 – 2025-01)

Conducted comparative study of AI/ML-enabled air defence systems

• Conducted a comparative study of AI/ML-enabled air defence systems, analyzing how computer vision, sensor fusion, predictive analytics, and autonomous decision-making improve threat detection, target classification, multi-target tracking, and real-time decision-making, strengthening my understanding of real-world AI applications in safety-critical environments

B.Tech in Artificial Intelligence and Machine Learning – Symbiosis Institute of Technology, Pune (2022-08 – 2026-05)