Projects

An AI agent that tailors resumes for every role, on autopilot which stitches leaves into a nest, this one stitches your experience to fit each job. An end-to-end system that turns a job-posting URL into a tailored, one-page resume (PDF + DOCX) with factual integrity, and does it autonomously at scale. You paste URLs into a local web tracker, click Run agent, and come back to finished resumes, dropped roles (with reasons), and a short list of judgment calls for you to decide. What inspired it? 1. The tailoring method comes from the open-source /resume-tailoring Claude Code skill (MIT, by Varun R). Tailorbird wraps that skill in automation rather than reinventing it, so quality equals a careful, hand-guided session. 2. The agent-over-skill design (deterministic Python for the mechanical 80%, the model only for authoring) came from watching where tokens and time actually went, and from the skill's own "token optimization" guidance. 3. The human-in-the-loop needs_review valve exists because some calls (an unusual level, a missing must-have, a borderline fit) genuinely need a person, so the agent parks those instead of guessing. Who benefits. 1. High-volume job seekers - apply to many roles without the per-role grind; a batch that took an evening now runs in minutes while you do something else. 2. Career switchers - the archetype/reframing engine surfaces transferable experience for roles adjacent to your background. 3. Non-native resume writers - get clean, idiomatic, role-matched phrasing without fighting the blank page. 4. Busy people - schedule it, results and a review list are waiting when you're back. 5. Anyone who values honesty - truthfulness boundaries are enforced; it will flag a poor fit rather than pad it.

Production portfolio with Avocado AI, a streaming RAG agentic chatbot backed by a 4-stage hybrid retrieval pipeline: query expansion (up to 4 variants), batched dense search via ChromaDB (all-MiniLM-L6-v2 ONNX), BM25 lexical search (rank_bm25), and Reciprocal Rank Fusion (k=60), all before Gemini 2.5 Flash sees the question. Implement model fallback mechanism via Gemini, Grow and OpenRouter with the knowledge base of ~80 atomic documents auto-syncs incrementally: new or edited documents are upserted, deleted documents are purged from ChromaDB, unchanged ones are skipped entirely. Runs as a FastAPI Docker container on AWS Lightsail with zero-downtime blue-green deployments, Nginx + Let's Encrypt HTTPS, daily S3 backup of SQLite analytics, and a Next.js 16 static frontend on GitHub Pages.

Achieved 15ms token latency on Snapdragon NPUs, a 10× improvement over cloud inference, by fine-tuning Llama 3.2 3B on security logs with QLoRA and deploying via 4-bit AWQ quantization through ONNX Runtime on-device. Guaranteed zero data loss during network partitions via an offline-first SQLite buffer with background sync workers.

AI-powered genomics discovery platform currently being built at NYU College of Arts and Science. Focused on making genomic data exploration accessible and actionable for researchers.

In active development at NYU CAS.

Production LangGraph + Llama 3.1 70B system that semantically maps global researcher collaboration networks by indexing millions of papers from Elsevier's Science Direct/Scopus. Cut P99 RAG latency by 78% with Write-Through Redis caching; sustained 99.9% uptime at 3,000+ RPS on AWS ECS.

Conflict-free simultaneous multi-user editing using Yjs (CRDTs) and WebSockets, scaled horizontally via Nginx load balancing across containerized instances. Increased AI auto-complete context quality by 65% with a Context-Aware Coding Agent using AST-based chunking and Voyage-Code-2 embeddings.

High-traffic progressive web app for academic grade forecasting at VIT. Scaled to 17K+ monthly active users and around 20K+ registered accounts. Achieved #2 Google Search ranking via programmatic SEO with sub-second mobile load times.

After six years in production and 17,000+ monthly users, gradeVITian has been rebuilt from the ground up, re-engineered on Next.js and FastAPI and relaunched at gradevitian.jayaremala.com. Heartfelt thanks to the VITian community (Class of 2020–2025) for making the original such a success; this journey defined me as an engineer, and this new chapter carries it forward.

The website for my father's fly ash brick yard in Kavali, Andhra Pradesh. First built during COVID to help people find the business, and rebuilt in 2026: bilingual in English and Telugu, WhatsApp-first enquiries, and local SEO so a customer twenty kilometres away can find the yard at all.

At its heart, this was a project of purpose: an online gateway built to help people navigate my father's new business within our town. Seeing a simple web application drive real-world sales and provide stability for my family during the COVID-era was a transformative experience. It taught me that engineering is most powerful when it serves a personal and communal need. Six years on, it has been rebuilt from the ground up and relaunched at vrfbricks.jayaremala.com. Bilingual in English and Telugu, because Kavali is a Telugu market. WhatsApp instead of a contact form, because that is how the construction trade actually talks. And the one real photograph of the yard is finally the first thing you see.