Software Engineer with experience building production backend systems, distributed services, real-time data pipelines, and AI-powered applications across Pacific Life, Uber, Hitachi Vantara, and California State University, Fullerton.

My work has focused on the engineering problems that matter in production: latency, reliability, scalability, security, observability, and maintainability. I have built and optimized systems using Java, Spring Boot, Python, Kafka, Redis, PostgreSQL, Kubernetes, AWS, and Azure, while also developing practical AI applications with LangChain, RAG, vector search, PyTorch, and Hugging Face Transformers.

I’m especially interested in backend engineering, distributed systems, platform engineering, and applied AI, where strong software fundamentals meet high-impact production problems.

• ⚙️ Backend Engineering — Java, Spring Boot, Python, FastAPI, Node.js, REST APIs
• 🌐 Distributed Systems — Kafka, Redis, microservices, event-driven architecture
• ☁️ Cloud & Infrastructure — AWS, Azure, Docker, Kubernetes, Terraform, CI/CD
• 🤖 AI Engineering — LLMs, LangChain, RAG, vector search, Hugging Face, PyTorch
• 📊 Data Engineering — PostgreSQL, MySQL, MongoDB, Spark/PySpark, Apache Hudi
• 🔐 Production Reliability — OAuth 2.0, JWT, RBAC, Prometheus, Jaeger, automated testing

Newport Beach, CA | Jun 2025 – May 2026

• Engineered Java and Spring Boot loan portfolio validation services for commercial and residential lending platforms, exposing REST APIs used by React dashboards for 300+ internal users and removing the need for direct SQL access by operations teams.
• Reduced API p95 latency by 40% across Kubernetes-hosted services by introducing Redis caching for high-cardinality loan lookups and tuning Kafka consumer partitioning and batch-fetch behavior.
• Integrated LangChain-based anomaly detection into daily ingestion workflows to identify statistical outliers and surface validation issues before downstream accounting processing.
• Shipped production TypeScript workflow approval and exception-handling components through GitHub Actions CI/CD while maintaining WCAG 2.1 AA compliance and contributing to zero regression incidents across four production cycles.

Fullerton, CA | Sep 2024 – May 2025

• Developed a MERN-based real-time monitoring platform supporting 100+ connected devices, including REST APIs, telemetry, alerting, and observability capabilities.
• Built a containerized room discovery and booking service with full-text search and filtering, deployed using Docker and Kubernetes and optimized for concurrent campus usage.
• Researched and prototyped practical AI applications including RAG-based assistants and computer vision pipelines using deep-learning frameworks.
• Implemented a YOLOv8 hardware-detection system, optimizing image-processing and inference workflows for more reliable real-time recognition.

Bengaluru, India | Sep 2023 – Jul 2024

• Reduced merchant onboarding processing time from 5 days to under 24 hours by integrating real-time menu-processing modules into core Java microservices built within Uber's Bazel monorepo.
• Revamped multi-fulfillment order tracking across delivery, grocery, and pickup workflows, using Redis caching and PostgreSQL tuning to reduce peak request latency by 20%.
• Strengthened distributed-service security by standardizing OAuth 2.0 and RBAC access models and integrating Prometheus and Jaeger telemetry to accelerate production incident triage.
• Built batch and real-time analytics ingestion pipelines with Python and PySpark over Apache Hudi datasets and supported automated staging deployments through Spinnaker CI/CD.

Bengaluru, India | Jan 2023 – Aug 2023

• Developed backend data-transformation plugins for the Pentaho platform using Java and SQL.
• Optimized relational query execution to improve reporting-module performance by 15%.
• Built automated integration and interoperability test scripts using Python.
• Supported containerized service deployments across Docker and internal Kubernetes staging clusters for CI/CD regression cycles.

AI-powered memory retrieval system that combines semantic search with graph relationships to reconstruct contextual timelines from stored information.

• Built FastAPI services around LangChain-powered RAG workflows.
• Combined Qdrant vector search with Neo4j graph relationships for hybrid retrieval.
• Used PostgreSQL for structured persistence across a multi-thousand-entry corpus.
• Improved contextual retrieval quality compared with flat semantic search by combining graph and vector relationships.

Tech: Python FastAPI LangChain Neo4j Qdrant OpenAI PostgreSQL

Event-driven backend prototype for real-time driver matching and ETA estimation.

• Built matching services with Java and Spring Boot.
• Used Kafka to process driver and rider location events asynchronously.
• Applied Redis Geo and geospatial PostgreSQL queries to rank nearby drivers.
• Deployed the system on Kubernetes with Redis caching for low-latency matching workflows.

Tech: Java Spring Boot Kafka Redis Geo Kubernetes PostgreSQL

M.S. Computer Science
May 2026 | GPA: 3.7 / 4.0

B.Tech
May 2023 | GPA: 3.68 / 4.0