Hi! I'm Varun 👋🏼

Engineered a privacy-native Generative AI platform that runs entirely on local silicon, eliminating cloud dependencies and data latency. The system orchestrates a microservices architecture (Docker) connecting a vector database (Qdrant), a local LLM inference engine (Ollama/Llama 3), and asynchronous file-system watchers (Watchdog) to ingest and index multi-modal data streams in real-time. Designed a dual-pipeline ingestion engine that separates high-level user profiling (via Mem0) from raw documentation indexing, enabling millisecond-latency semantic search across gigabytes of proprietary data.

Designed an automated Role-Based Access Control (RBAC) system using complex SQL/PL-SQL stored procedures to govern Power BI data security. Institutionalized Test-Driven Development (TDD) protocols for analytics modules, establishing rigorous unit and integration testing standards that eliminated regression defects.

Architected an event-driven microservices ecosystem for high-volume log ingestion, utilizing Docker, FastAPI, and RabbitMQ to ensure asynchronous processing resilience. Integrated a self-hosted Hugging Face model for real-time semantic enrichment, channeling data into an Elasticsearch-backed React dashboard for live visualization and full-text search.

Developed an autonomous, privacy-centric code refactoring agent utilizing LangChain and Llama 3.1 via Ollama for fully local execution. Architected a ReAct-based workflow integrated with ChromaDB for retrieval-augmented generation (RAG), enabling the agent to maintain cross-file context during static analysis. Implemented robust fail-safes using Python’s AST module to enforce strict syntax validation and prevent regression errors prior to committing file modifications.

Engineered high-availability solutions for enterprise data pipelines, resolving complex race conditions in REST API workflows and database synchronization. Optimized query performance and data ingestion latency across distributed mobile/web platforms, directly hardening the product architecture against connection timeouts and SSL failures.

Developed detailed technical documentation, including setup guides, feature specifications, and release notes, by distilling complex engineering concepts into precise, user-oriented content, while standardizing terminology, diagrams, and workflows to enhance cross-team collaboration among developers, QA, and customer success.

Designed an Autonomous RAG Agent to eliminate source hallucination and automate academic curation within a local-first Llama 3 environment. Implemented a custom ETL workflow using n8n and JavaScript to physically stamp metadata onto vector embeddings in Qdrant, effectively resolving context fragmentation. This architecture delivers verifiable citation accuracy while demonstrating advanced end-to-end system ownership.

Developed an autonomous, privacy-centric code refactoring agent utilizing LangChain and Llama 3.1 via Ollama for fully local execution. Architected a ReAct-based workflow integrated with ChromaDB for retrieval-augmented generation (RAG), enabling the agent to maintain cross-file context during static analysis. Implemented robust fail-safes using Python’s AST module to enforce strict syntax validation and prevent regression errors prior to committing file modifications.

Architected a scalable Pega-based enterprise framework supporting 10,000+ concurrent users, integrating legacy systems via SOAP/REST web services for real-time data synchronization. Implemented zero-downtime deployment strategies using Pega Deployment Manager and enforced strict TDD patterns to ensure architectural resilience.

Engineered a fully offline, privacy-centric Retrieval-Augmented Generation (RAG) system using React, Flask, and Docker to host Meta’s Llama 3. Architected a persistent containerized vector pipeline utilizing ChromaDB to enable dynamic context ingestion and retrieval from local datasets. This solution demonstrates end-to-end ownership of secure generative AI infrastructure without reliance on external APIs.

Developed a local-first, reactive notebook environment that unifies backend logic and frontend UI execution within a single interface. Engineered a hybrid runtime architecture combining direct local Node.js access with sandboxed polyglot support (Python, Go) via the Piston API. Implemented a spreadsheet-like reactive data graph to automate state synchronization between backend processes and React components, significantly streamlining the full-stack prototyping workflow.

Conducted a comprehensive analysis of current literature contrasting the resurgence of ConvNeXt architectures against Vision Transformers (ViTs) alongside the paradigm shift toward Self-Supervised Learning. Identified critical technical gaps regarding model robustness against adversarial examples and data efficiency within medical imaging applications.