Hyderabad, Telangana, India
Senior Full Stack Engineer with 4+ years of experience designing, developing, and maintaining scalable enterprise applications using Java, Spring Boot, ReactJS, Node.js, and Microservices architecture. Currently at Hiringhood, where I lead the design and architecture of HR-tech and recruiting platforms — building secure RESTful APIs, integrating MongoDB and SQL databases, and deploying containerized microservices on AWS and Azure with Docker, Kubernetes, and CI/CD pipelines via GitHub Actions and Jenkins. — What I bring — Backend: Java · Spring Boot · Node.js · Express · RESTful API Design · Microservices Frontend: ReactJS · Vue.js · AngularJS · JavaScript · TypeScript Cloud & DevOps: AWS (Lambda, ECR, EC2, S3, Cognito) · Microsoft Azure · Docker · Kubernetes · CI/CD (GitHub Actions, Jenkins) Databases: MongoDB · PostgreSQL · MySQL · SQL Server AI / ML: LLM Integration · YOLO Object Detection · Pre-trained Model Deployment Methodologies: Agile · Scrum · Test-Driven Development · Code Reviews — Highlights — → Architected an end-to-end HR SaaS platform on a Microservices architecture, cutting resume processing time by 30% and improving recruiter efficiency by 25%. → Built RESTful APIs for the Talent Xchange C2C platform — 40% reduction in data processing time, enabling real-time talent acquisition. → Integrated Large Language Models (LLMs) to automate resume screening and candidate matching — 50% reduction in manual recruiter effort. → At Must Analytics (Berlin), developed an AI-powered Insurance Claims Damage Assessment Platform using YOLO v3 Object Detection to classify vehicle damages from customer claim images — 60% reduction in inspection time, plus automated fraud-detection signals. → Mentored a team of 6+ developers on architectural decisions, design patterns, and feature implementations. I care about clean architecture, Test-Driven Development, and using AI thoughtfully to solve real business problems. I'm currently exploring opportunities in insurance, financial services, and enterprise SaaS — domains where engineering rigor genuinely matters. Let's connect if you're working on: • Microservices at scale • Cloud-native modernization (AWS / Azure) • AI-integrated enterprise platforms • Insurance or financial services tech Reach me: [email protected]
▸ Design and architect end-to-end Full Stack enterprise applications using Java, Spring Boot, ReactJS, Node.js, TypeScript, MongoDB, and SQL on a Microservices architecture. ▸ Architected an HR SaaS platform on Microservices, streamlining Corp-to-Corp (C2C) hiring by parsing resumes, identifying candidate capabilities, and shortlisting top talent. ▸ Built and deployed Smart Recruiter Applications — Resume Watermarker, Resume Parser, Talk to Resume, Role Mapper, Recruiter Co-Pilot — using Java/Spring Boot, Node.js, ReactJS, and MongoDB. Achieved 30% reduction in resume processing time and 25% improvement in recruiter efficiency. ▸ Designed secure RESTful APIs across Java/Spring Boot and Node.js microservices for the Talent Xchange C2C platform, integrating MongoDB and SQL — cutting data processing time by 40%. ▸ Integrated Large Language Models (LLMs) to automate resume screening and candidate matching, reducing manual recruiter effort by 50%. ▸ Engineered cloud-native deployments on AWS (Lambda, ECR, EC2, S3) and Microsoft Azure with Docker, Kubernetes, GitHub Actions, and Jenkins CI/CD for zero-downtime releases. ▸ Optimized backend APIs via caching, query tuning, and indexing on MongoDB and SQL, reducing API latency and infrastructure cost. ▸ Conducted code reviews enforcing clean architecture, SOLID principles, and TDD. Wrote unit and integration tests with JUnit, Mockito, Jest, and Supertest, maintaining >80% coverage. ▸ Led Agile/Scrum delivery in 2-week cycles, mentoring 6+ developers on architectural decisions, microservice design patterns, and feature implementations.
Project: Insurance Claims Damage Assessment Platform ▸ Developed an AI-powered insurance claims assessment platform leveraging YOLO v3 Object Detection to automatically identify and classify vehicle damages — including dents, scratches, broken headlights, windshield damage, and bumper damage — from customer-uploaded claim images, reducing manual preliminary inspection effort. ▸ Designed backend services using Node.js, Express, and GraphQL, exposing secure REST and GraphQL APIs that insurance adjusters used to inspect, validate, and approve claims at scale. ▸ Integrated the YOLO v3 deep learning model and built image processing workflows on AWS Lambda, optimizing model inference for real-time claim evaluations. ▸ Built underwriter-facing dashboards using Vue.js, allowing adjusters to review damage reports, view bounding boxes on uploaded images, and approve or reject claims with full audit trails. ▸ Stored claim evidence, damage reports, and assessment metadata in MongoDB, ensuring traceability and compliance with insurance data retention policies. ▸ Containerized AI services using Docker and deployed images via AWS ECR, enabling scalable, reproducible, and isolated execution environments for production AI workloads. ▸ Implemented image metadata validation and confidence-threshold filtering to flag potentially fraudulent claim submissions, contributing to a 60% reduction in claim inspection time and improved fraud-detection accuracy.
▸ Researched and analyzed automated locking mechanisms for hotels, evaluating smart-lock technologies to implement secure and efficient guest access solutions. ▸ Conducted a comprehensive study on communication protocols — Bluetooth, Wi-Fi, NFC, ZigBee — for check-in data transmission, comparing connectivity approaches between mobile applications and front-desk check-in devices. ▸ Designed and developed an automated check-in system integrating Lodgit Desk hotel software with smart locking systems and guest authentication, enhancing hotel operations and improving user experience.
Project: Parallel Build of ELF — a multi-threaded extension of a state-of-the-art tree structure used to accelerate multi-predicate selection queries in analytical (OLAP) databases. ▸ Goal: Implement a parallel (multi-threaded) build of the ELF index and benchmark its performance against the standard single-threaded ELF baseline. ▸ Context: Analytical database queries are increasingly compute-intensive while single-core CPU clock speeds have plateaued — making multi-core parallelism the most impactful path to reclaim headroom. ▸ Original ELF is a highly optimized prefix-sharing tree structure that targets bare-metal speed for multi-predicate selection queries — but its single-threaded design leaves modern multi-core CPU capacity underutilized. ▸ Designed and implemented parallel construction algorithms that split the tree-build workload across CPU threads while preserving the prefix-sharing invariants of the original ELF data structure. ▸ Handled low-level concerns including thread synchronization, race conditions, memory layout, and cache locality to maintain ELF's bare-metal performance characteristics under concurrent access. ▸ Benchmarked Parallel ELF against the single-threaded baseline across varying thread counts and dataset sizes, measuring build time, query latency, and throughput. ▸ Demonstrated meaningful speed-ups on multi-core hardware, validating multi-threading as an effective optimization layer on top of an already heavily tuned data structure. ▸ Gained deep hands-on experience with parallel programming, performance engineering, database index internals, algorithm design, and rigorous experimental benchmarking.
Worked under Dr. David Broneske ▸ Conducted hands-on training in SQL and MySQL, guiding students in database management, query optimization, and relational database design. ▸ Led weekly exercise classes for 30 students, independently delivering practical sessions using SQL Developer, enhancing technical proficiency in database fundamentals. ▸ Provided structured mentorship, helping students master indexing, stored procedures, transaction processing, and best practices for efficient SQL query execution.