I am an AI/ML systems developer and full-stack software engineer who bridges the gap between machine learning research and scalable production deployments.
Currently employed as Tech Co-Lead at Campus Konnect (startup) directing web engineering and UI architecture. In addition, I have completed 6 intensive engineering internships and contribute across 7 premier university technical societies and research labs (including USC.KIIT, GeeksforGeeks KIIT, Coding Ninjas, K-1000, AIPG, Kinetex Lab, and E Labs) spanning AI systems, full-stack architecture, and generative AI pipelines. I've engineered everything from AST-driven autonomous code analyzers (Top 10% National Hackathon) to cryptographic socket protocols with AES-256-GCM encryption and enterprise demand forecasting models.
My foundation combines deep mathematical rigor in statistical learning with high-throughput backend infrastructure in Python, Node.js, React, Docker, and Cloud architectures (Google Cloud, AWS).
Employed as Tech Co-Lead at Campus Konnect (venture startup). Leading core web engineering strategy, front-end platform architecture, and cross-functional technical teams. Spearheading responsive UI component libraries, standardizing full-stack development pipelines, conducting code reviews, and managing agile engineering sprints.
Dual technical charter across the university council: architecting official council web applications and deploying Robotic Process Automation (RPA) workflows coupled with AI models to automate campus operations, data pipelines, student administration, and technical event registrations.
Core technical team developer responsible for designing and deploying official chapter web applications, interactive contest interfaces, and event landing pages. Implementing performant, accessible web components for university coding bootcamps and hackathons.
Engineering chapter web infrastructure, student registration portals, and interactive user interfaces for campus programming contests, mentorship sessions, and technical community hackathons.
Developing machine learning prototypes, statistical data pipelines, and predictive models within the campus technology incubator. Collaborating on algorithm optimization, model validation, and applied deep learning implementations.
Collaborating on advanced deep learning architectures, generative AI research pipelines, and neural predictive modeling. Engaging in peer model benchmarking, research evaluations, and algorithmic explorations.
Architecting AI/ML modules and intelligence engines for research lab initiatives. Focused on integrating machine learning pipelines with production interfaces and conducting empirical evaluations on multimodal datasets.
Engineering applied artificial intelligence pipelines, intelligent embedded systems, and transformer-based models. Building real-time inference workflows and collaborating on multidisciplinary laboratory prototypes.
Awarded Star Performer recognition for exceptional delivery. Architected and deployed 3 core projects: Nova Voice Assistant with real-time speech synthesis, a multithreaded TCP socket chat engine, and an interactive BMI telemetry application.
Built end-to-end machine learning and analytics engines: Real-Time Credit Card Fraud Detection (0.8886 ROC-AUC), Multi-Symptom Healthcare Disease Recommender, and IPL Cricket Match Telemetry Dashboard with interactive Plotly visualizations.
Developed production NLP and Vision microservices: Streamlit-based Groq LPU Llama 3.3 70B Assistant, Google Translate API Neural Multi-Language Translator (50+ languages), and In-Browser Computer Vision Gesture Recognition with TensorFlow.js.
Completed all 8 full-stack engineering milestones: Node.js + Express backend infrastructure, client/server validation pipelines, REST API architecture with MongoDB JWT authentication, live weather integration, and production caching middleware.
Shipped 5 AI modules in 30 days: Rule-Based RegEx NLP Chatbot, Unbeatable Minimax Tic-Tac-Toe AI, OpenCV Haar Cascade Face & Eye Detection, BLIP Transformer Image Captioning with PyTorch, and TF-IDF Movie Recommendation Engine with Cosine Similarity.
Engineered production Generative AI applications leveraging LLM orchestration, structured prompt engineering, vector database embeddings, and RAG document intelligence workflows for multi-agent systems.
Whether you have a technical opportunity at your company, an open-source project, or want to discuss machine learning architectures, I'm always open to connecting.
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