Professional backend and distributed systems engineer with expertise in building high-scale, fault-tolerant platforms. At Project44,I has engineered large-scale data pipelines processing 1.4M+ events/day, asynchronous onboarding systems for 50,000+ carriers, and real-time webhook synchronization services used across global logistics operations.I am also the creator of SlazySloth, an AI-powered DSA and interview preparation platform featuring 350+ problems, real-time progress tracking, LangChain/LangGraph–based interview simulations, AWS microservices, and intelligent recommendation models. With a strong foundation in Java, Spring Boot, Python, C++, Kafka, Kubernetes, AWS, Snowflake, and scalable system design,I specializes in building low-latency, high-reliability services.

Building high-scale backend systems and low-latency distributed architectures. Cloud engineering on AWS/GCP with strong focus on DevOps automation and reliability AI/LLM engineering, especially LangChain/LangGraph-based interview and learning tools High-performance programming, algorithms, and competitive problem solving Observability, monitoring, and performance tuning of production-grade services

https://www.slazysloth.com Building an advanced learning platform with 359+ DSA problems, real-time progress tracking, and an AI interview simulator powered by LangChain & LangGraph. Enhancing the architecture with AWS microservices, Dockerized code execution, SQS pipelines, and personalized recommendations. Experimenting with an LLM-based system that generates HLD/LLD diagrams, architecture flows, and design reviews to help engineers prepare for interviews and build real projects faster.

I am a Software Engineer at Confluent focused on scaling data streaming through advanced Kafka Connectors, having recently enhanced the HTTP v2 Source Connector with chaining-based timestamp pagination. My professional foundation was built over nearly two years as a founding engineer at Alltius.ai, where I architected complex distributed systems using technologies like Redis pub-sub and Temporal, and developed advanced, performance-optimized agentic RAG AI models. I thrive in high-impact, early-stage environments where I can drive core architecture, and I am currently seeking similar opportunities that offer significant technical challenges and exponential growth.

I am keen to explore and collaborate on the next wave of intelligent, automated systems. My primary areas of interest include Agent Swarms and Agent-to-Agent Interaction, focusing on developing the communication protocols and orchestration frameworks needed for multiple specialized agents to solve complex, multi-step problems autonomously. I am also researching applications in Voice AI for advanced multimodal interaction, and leveraging LLMs to power next-generation Robotic Process Automation (R

I am currently extending the principles from my successful goal-tracking and classification model to develop a Real-time Contextual Agent. This project involves building a lightweight browser extension that uses the previously optimized machine learning model to perform instant, localized classification of browsing data. This is integrated with a cloud-based action pipeline (using a minimal serverless workflow) designed to trigger intelligent, automated outputs—such as real-time content summariz