I am an AI Engineer and Full Stack Software Engineer with more than 3 years of experience building production-scale Generative AI applications, conversational AI systems, distributed backend platforms, and cloud-native ML infrastructure.
I have worked extensively across AI/ML development, LLM-powered workflows, Retrieval-Augmented Generation (RAG), prompt engineering, AI agents, model evaluation, feature engineering, and scalable data processing pipelines. My experience includes using Azure OpenAI, Azure ML, AWS SageMaker, Kubernetes, and related cloud technologies to deliver reliable production systems.
In my current role at Qualitest Group, I build AI-powered validation and data quality workflows, scalable ingestion pipelines, and RAG solutions that improve labeling accuracy and reduce manual effort. I focus on production readiness, automation, and collaboration with ML engineers and data scientists to move solutions from prototype to deployment efficiently.
Previously, I worked as a Full Stack Engineer at Saayam For All, where I developed features for a high-traffic platform, built distributed backend services, optimized PostgreSQL and Redis performance, and delivered secure low-latency APIs. I also spent over two years at Cognizant Technology Solutions modernizing dashboards, building financial tracking systems, and improving application performance and reliability.
My project work includes AI interview coaching, self-healing AIOps, schema-to-SQL generation, and an AI persona portfolio. These projects reflect my interest in combining intelligent automation, user-facing product design, and scalable engineering practices.
I hold a Master of Science in Computer Science from California State University, Long Beach, and I have earned certifications in Azure AI Engineering, AWS Machine Learning, and CCNA. I am based in San Francisco, California, and I work across AI engineering, full-stack development, and cloud infrastructure.
Reduced manual review by 40% and cut ML dataset prep time by 3x by engineering LLM-powered validation pipelines. Developed AI-powered data quality and validation workflows using Azure OpenAI, Python, and Azure ML. Designed scalable ML data ingestion pipelines using Azure AKS, Azure ML, and Apache Kafka. Built a RAG pipeline using Azure OpenAI and Pinecone. Collaborated with ML engineers and data scientists to deploy and monitor production AI/ML solutions.
Developed full-stack features for a platform serving 50K+ monthly active users. Built distributed backend services with Node.js and PostgreSQL for real-time donation tracking and campaign management. Optimized PostgreSQL indexing and Redis caching to reduce latency. Delivered low-latency REST APIs with JWT authentication and RBAC.
Modernized the CXO Dashboard using .NET, Angular, and SQL Server. Built a Portfolio Management System with React.js, Flask, and GraphQL. Migrated legacy SQL stored procedures to scalable data models and built reusable Angular components. Engineered JWT and OAuth 2.0 role-based authentication and integrated RESTful and GraphQL APIs. Partnered with QA and release teams to close testing gaps and reduce defects.
Master of Science in Computer Science
Full-time program. Relevant coursework included Machine Learning, Distributed Systems, and Natural Language Processing.
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