Full-stack developer and technical lead who drives execution from vision to business impact — specializing in scalable, distributed architecture, cloud-native solutions, and ML/AI integration
Architectural patterns, system designs, case studies and real-world implementations — each focused on demonstrating specific technical decisions and approaches
Full-Stack AI System Design
Implemented as an in-memory, event-driven, async pipeline, the system uses asyncio queues for inter-stage data flow, configurable event routing, and generator-compatible stages for streaming. Each conversation runs an isolated pipeline with its own async tasks, WebSocket connection, and pluggable backends for STT, LLM, and TTS. A separate control plane provides pub/sub state updates and awaitable control signals for cross-stage coordination. The system supports multiplexing multiple STT/TTS processing over a single backend.
Architecture Patterns
Technologies
Knowledge Indexing and Technical QA System Design
A pipeline for transforming unstructured technical data into a query-optimized knowledge base. System performs semantic chunking, SVO triplet extraction, and SVO-constrained generation to create precise, low-cost QA pairs. A distilled extractive QA model validates and filters generated pairs, while redundancy is reduced via Affinity Propagation clustering. Retrieval combines dual-scale embedding (QA + chunk level) with hybrid dense, sparse, and late-interaction (ColBERT) scoring for both granular and broad technical search.
Architecture Patterns
Technologies
Architecture Design Case Study
This case study focuses on a ticketing system for ultra-large-scale events, handling 100K seats, 1M+ RPS and >10M potential concurrent users (queue). Addresses architectural decisions from multiple angles (DB limits, CAP, UX, Fairness, Exploitation) and discusses tradeoffs, queue management strategies, database partitioning, caching and failure scenarios. Includes load calculations, cost modeling, and scalability analysis across multiple architectural approaches.
Architecture Patterns
Technologies
Distributed Microservices
High-availability microservice based URL shortener with QR generation and real-time analytics dashboard. Built on serverless edge architecture demonstrating CAP theorem tradeoffs, global distribution, and cost optimization. In addition, the project contains an Admin SPA (React).
Architecture Patterns
Technologies
ML Model Training
Parameter-efficient fine-tuning of code generation model for domain-specific tasks. Demonstrates understanding of modern ML training pipelines, transfer learning strategies, and Hugging Face ecosystem integration.
Architecture Patterns
Technologies
Data Science Workflows
End-to-end data science workflows considering real world business objectives in Loan approval, Bike sharing and Telecom industry. Preprocessing pipelines, exploratory analysis, feature engineering with RFE/RFECV, and production ML model training (Random Forest, Gradient Boosting) with hyperparameter optimization.
Architecture Patterns
Technologies
My approach centers on establishing lightweight processes that create clarity. I apply agile methodologies through principles, not rigid ceremonies — standups that unblock work, retros that drive change, and planning that serves delivery. I coach teams through complex technical decisions and maintain the engineering standards that turn code into reliable products.
Whether designing distributed systems, integrating ML/AI solutions, or scaling high-availability infrastructure, I focus on delivering features that drive business value and choosing architecture that fits the product stage — from PoC to MVP to scaling for millions of users.
When authority is needed to protect quality or timelines, I use it — but I'd rather influence through collaboration . I can work across product, design, marketing, and operations to align priorities and translate between technical execution and business strategy. I've learned that the best solutions emerge when engineers understand the "why" behind the work, and stakeholders understand the trade-offs in the "how."
AWS Solutions Architect Professional and ML/AI Specialization certificates to formalize my ongoing learning and personal development.