Dhriti Singh

Software Engineer

Surat, Gujarat, India1 yr 7 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

1+ years of experience in software development.
Expertise in building full-stack applications using the MERN stack.
Skilled in developing RESTful APIs and AI-driven solutions.

Stackforce AI infers this person is a Full-Stack Developer with a focus on AI and SaaS solutions.

Contact

dhriti.singh250@gmail.com LinkedIn

Skills

Core Skills

Full-stack DevelopmentApi Development

Other Skills

ADKAI AgentsC++Computer VisionEmbedded SystemsExpress.jsGenerative AIGoogle Cloud Platform (GCP)JavaScriptLLMsLangChainLanggraphMachine LearningMicrosoft ExcelMongoDB

About

🎯 I’m a software developer with 1+ years of experience, driven by curiosity and a passion for building impactful tech solutions.• Experienced in developing RESTful APIs, leveraging LLMs, building GenAI pipelines, and delivering full-stack applications using the MERN stack.• Skilled in problem-solving, backend architecture, and working with data to design intelligent and efficient systems.• Strong foundation in computer science and hands-on experience in building robust, scalable and user-friendly applications.

Experience

Keyjavi, llc

Software Engineer

Apr 2025 – Present · 11 mos · United States

Integrated vector databases for AI-driven document processing.
Built and configured AI agents to handle specific tasks using LLMs and tools.
Developed agent logic for task delegation, tool usage, and automated decision steps.
Implemented an orchestration layer enabling LLM-driven agents to coordinate parsing, embedding, retrieval, and report generation.
Built RESTful APIs using Node.js, Express.js, and MongoDB as the database.
Leveraged NLP, LLMs, and Generative AI to enhance system capabilities.
Designed a pipeline for real-time document upload, vectorization, retrieval, and match report generation.
Architected the AI-agent deployment workflow using GCP, vector databases, and APIs to enable automated, LLM-driven operations.
Developed RAG workflows combining vector databases and LLM-based response generation.