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$ whoami
Aneesh Venkatesha Rao — B.Tech ECE, NIT Warangal (2024–2028)

$ status
AI Engineer • Open Source Contributor • SIH 2025 National Finalist (Top 5 of 75,000+ submissions)

$ focus --areas
- AI/ML product engineering (RAG, LLM orchestration, retrieval systems)
- Full-stack application architecture
- Distributed data + automation pipelines
- Human-in-the-loop developer tooling

$ currently
Shipping Stratos for the IBM AI Builders Challenge, while building toward
open-source contributor credit (LFX Mentorship / GSoC).

I enjoy bridging research-minded experimentation with production-ready software. My approach combines AI/ML capability, backend rigor, and clean frontend execution to ship products that are both technically strong and genuinely usable.

Stratos — "Football, Understood"

An IBM AI Builders Challenge submission (due June 30, 2026) spanning two tracks: a tactical decision explainer and a multilingual fan companion. Built on IBM Granite / WatsonX, Docling, Langflow, and Context Forge, with a D3.js frontend backed by a stateless, per-call backend.

Tech: IBM Granite, WatsonX, Docling, Langflow, Context Forge, D3.js

🔗 Repository:

ContextCraft · AST-aware codebase search engine  

• Built a hybrid search engine that parses codebases with tree-sitter — functions and classes as atomic chunks, never mid-function splits
• Implemented Reciprocal Rank Fusion merging pgvector cosine similarity + PostgreSQL BM25, with per-repo RRF normalization for multi-repo queries
• Added Cohere cross-encoder reranking (20 → 10 candidates) and 1-hop dependency graph expansion with cycle detection across file boundaries
• Enriched every chunk with git blame author metadata; benchmarked at 80% source hit rate at 3.88s P50 latency across 30 queries
• Published to PyPI (pip install contextcraft-py); CI enforces mypy strict, ruff, and pytest before every merge
• Tech: Python, FastAPI, PostgreSQL, pgvector, tree-sitter, Cohere, Next.js, Docker

🔗 Repository: github.com/AneeshVRao/ContextCraft

Humanify · AI text humanizer — live, monetized SaaS  

• Built a production SaaS that rewrites AI-generated text to read naturally, with multi-AI routing across Gemini and Claude depending on input characteristics
• Shipped and currently runs a paid Pro tier (₹999/mo) with Razorpay billing integration
• Designed the full subscription lifecycle — auth, billing, plan gating — on Supabase
• Tech: Next.js, Supabase, Razorpay, Gemini API, Claude API

🔗 Repository: github.com/AneeshVRao/Humanify

Dev-Saarathi · Voice-first AI coding assistant  

• Built a voice-to-code pipeline in 11 Indian languages through transcription + intent detection
• Designed an AI orchestration flow (Intent Detection → Guardrails → Execution Router) for safe automation of code actions
• Implemented context-aware reasoning over 50+ files / 100k+ characters via RAG to reduce hallucinated outputs
• Added human approval controls while keeping 3–6s voice-to-response latency
• Tech: TypeScript, Python, AWS (Bedrock, Transcribe, S3), RAG

🔗 Repository: github.com/ashb155/dev-saarathi _{(team repo, hosted under collaborator's account)}

ShabdSetu · AI-powered multilingual learning platform  

• Built an end-to-end learning system for 10+ Indian languages using IndicTrans2 + Whisper
• Developed fuzzy pronunciation scoring for real-time speaking feedback
• Engineered layered caching (in-memory + Firestore) for sub-50ms response paths
• Added gamification with XP, streaks, and 27 achievements synced across authenticated sessions
• Tech: Next.js, React, FastAPI, IndicTrans2, Whisper, Firestore

🔗 Repository: github.com/AneeshVRao/ShabdSetu

Nexus Load Balancer · HTTP load balancer in Go  

• Built a lightweight HTTP load balancer from scratch with round-robin request distribution across backend pools
• Implemented active health checks (periodic probing) and passive health checks (live-traffic failure detection) to pull unhealthy backends out of rotation automatically
• Tech: Go, net/http

🔗 Repository:

Coursework and lab projects at NIT Warangal, several built alongside classmates Akula Sahasra, Adhvay Shrujal, and Arushi Pundir.

Bayesian In-Memory Compute (IMC) Core SRAM-based weight lookup, LFSR-driven stochastic sampling, a Kogge-Stone parallel-prefix popcount unit, and FSM control logic — implemented and verified in Vivado.

FPGA Brain Tumor Segmentation Otsu thresholding + watershed segmentation accelerated via Vitis HLS on a MicroBlaze soft core, achieving a 144–229× speedup over a software baseline. Deployed on Nexys 4 DDR / Artix-7.

Tech: Verilog, Vivado, Vitis HLS, MicroBlaze

• Build for clarity, then optimize for scale
• Treat performance as a core product feature
• Keep architecture modular and maintainable
• Use AI where it delivers measurable utility
• Design developer experiences that reduce friction

• 🥇 Smart India Hackathon 2025 — National Finalist (Top 5 in problem statement, selected from 75,000+ submissions)
• 📜 13 professional certifications across cloud, software engineering, and data science (Google, Meta, IBM)
• 🏅 65+ Olympiad medals in national-level Mathematics and Science competitions

Who is Aneesh Venkatesha Rao? Aneesh Venkatesha Rao is an AI engineer and full-stack developer, and a third-year B.Tech Electronics & Communication Engineering student at NIT Warangal, India (Class of 2028). He is a Smart India Hackathon 2025 National Finalist.

What has Aneesh built? Notable solo builds include ContextCraft (an AST-aware code search engine combining tree-sitter parsing, hybrid RRF search over pgvector and PostgreSQL BM25, and Cohere reranking, published to PyPI) and Humanify (a live, monetized SaaS that rewrites AI-generated text using multi-provider LLM routing). He has also contributed to team projects including Dev-Saarathi (a voice-first AI coding assistant) and ShabdSetu (a multilingual language-learning platform).

What technologies does Aneesh work with? Python, TypeScript, Go, C++, SQL, FastAPI, Next.js/React, PostgreSQL, pgvector, Docker, and LLM/RAG tooling including LangChain, Cohere, Whisper, and IBM Granite/WatsonX. He also has hardware engineering experience in Verilog and FPGA development (Vivado, Vitis HLS).

Is Aneesh open to internships or collaboration? Yes — he is actively looking for AI/ML product engineering internships, backend/full-stack roles, and open-source collaboration (targeting LFX Mentorship and Google Summer of Code contributor credit). Reach out via email or LinkedIn.

• 🌱 Open-source contributions — actively working toward LFX Mentorship / Google Summer of Code
• 🤖 AI/ML product engineering internships
• 🛠️ Backend / full-stack engineering roles
• 🔗 Collaborations in dev tooling, RAG systems, and developer productivity

If you're building something meaningful in AI, full-stack systems, or developer tooling, I'd love to connect.