Pratham Arora
Final-year CS & AI at Plaksha University. I build production ML systems — RAG pipelines, LLM agents, applied NLP. Researching where frontier VLMs fail at physical-world reasoning.
Available for full-time roles.
Experience
Freelance Web Developer — PQRS Research (Dr. Niket Tandon)
May 2026 – Present- Leading a full redesign and feature buildout of the PQRS Research website in Next.js, including a dual onboarding system with distinct registration and intake flows for mentors and mentees.
AI Engineering Intern — Cotality
June 2025 – July 202585%
Cost Reduction
50k+ LOC
Lines Per Day
15+ langs
Languages
12 APIs
Endpoints
- Architected a FastAPI backend with AST-based parsing for 15+ languages, enabling automated documentation generation across polyglot codebases at 50,000+ LOC/day.
- Cut AI embedding costs by 85% by building a hash-based system that fingerprints each function's AST and skips re-embedding unchanged code, eliminating redundant Azure OpenAI API calls.
- Built a RAG pipeline using LangChain and Azure OpenAI, backed by Cosmos DB with IVF vector indexing for semantic code retrieval –- migrated from FAISS to support persistent, production-scale storage.
- Shipped 12 production REST API endpoints with typed Pydantic schemas, powering real-time documentation preview in the client-facing web interface.
Freelance Web Developer — Anuj Desai Associates
May 2025- Delivered a full-stack site with a public careers page and a Firebase-backed admin dashboard, giving firm partners a private portal to review and manage job applications.
- Secured admin access via Google OAuth with an email allowlist (Firebase Auth), enforcing partner-only access without a separate auth backend.
- Built a GitHub-editable content layer so the client can update site copy post-handoff without developer involvement.
Software Development Engineer Intern — Orangewood Labs (Robotics)
June 2024 – July 2024- Designed modular LLM task-planning system for a food-preparation robotic arm, decoupling high-level intent logic from low-level actuation to enable parallel development across firmware and AI teams.
Projects
View all ↗VR LLM Conversational Agent
AI/MLMost VR agents have 3–5 second response latency — long enough to break immersion. I built a conversational agent that hits 1.8s average by parallelizing Gemini 2.5 Flash calls with Google Cloud STT/TTS, caching partial results, and preprocessing audio before transmission.
AI Resume Builder & Interview Prep Tool
Full StackTailoring a resume for each role is tedious and opaque. This tool takes your resume and a job description, generates an ATS-optimised version using Gemini 2.5 Flash via streaming API, and surfaces role-specific interview questions by matching your experience against a structured DSA/STAR prep database.
Kelp Forest Semantic Segmentation
AI/MLKelp forests are a critical ocean ecosystem — detecting them in satellite imagery is hard because atmospheric interference corrupts images unpredictably. I trained a U-Net with EfficientNet-B3 backbone for segmentation and built a streak-detection pipeline to filter corrupted training images before they hurt model accuracy.
Anuj Desai Associates — CA Firm Website
Full StackDesigned and shipped a full-stack site for a CA firm with a careers page and a private admin dashboard where firm partners review and manage candidate applications, powered by Firebase Realtime Database.
Research
Undergraduate Researcher — Plaksha University
Jan. 2026 – PresentVisual Benchmarking of VLMs | Sup. Prof. Pankaj Pansari
- Constructed a 459-image benchmark with instrument-verified ground truth across 5 physical estimation tasks; personally handling all instrument measurements and building a 3-tier blur-degradation pipeline (OpenCV) to test model perceptual robustness.
- Benchmarked 6 VLMs (Gemini 3.1 Pro, GPT-5.4, Gemini-Robotics-ER, Claude Opus 4.7, Gemma 4, Qwen 3.5) across 3 independent runs each, finding performance collapses significantly under image blur across all frontier models.
- Identified a misidentification-vs-miscalibration failure dichotomy via top-10 error analysis per category: GPT-5.4 fails by misidentifying objects, while Gemini variants correctly identify but overestimate weights by 1.8–2.5.
Undergraduate Researcher — Plaksha University
June 2023 – Aug. 2023Supervised by Prof. Sandeep Manjanna
- Developed preprocessing pipeline (contrast adjustment, noise reduction, edge detection) to optimise Segment Anything Model (SAM) for agricultural crop-weed segmentation on sparse datasets, maximising zero-shot segmentation quality on out-of-distribution agricultural imagery.
- Improved batch processing throughput for 10,000+ image datasets by implementing vectorized NumPy operations and Python multiprocessing.