TSH Company | AI Prompt Engineer & Agent Architect | June 2025 – Present
• Architected Agentic Workflows: Developed autonomous AI agents using LangChain and Python
to automate lead qualification and data extraction, reducing manual workload by 15+ hours per
week.
• RAG System Development: Integrated Retrieval-Augmented Generation (RAG) using Vector
Databases to allow agents to query internal documentation, increasing response accuracy by
50%.
• Cost & Performance Optimization: Designed and A/B tested Chain-of-Thought (CoT) system
prompts for GPT-4, optimizing token usage and reducing monthly API expenditures by 20%.
• Cross-Functional Collaboration: Partnered with Product and Engineering teams to embed AI
features into existing SaaS modules, resulting in a 25% increase in user engagement.
TECHNICAL PROJECTS
AI Chatbot with RAG Implementation | Python, FastAPI, JavaScript, Docker
• Built a full-stack RAG application capable of processing and querying large PDF/Text datasets.
• Developed a high-performance backend with FastAPI and containerized the entire stack using
Docker for seamless deployment.
Microservices Architecture with Kafka | Python, FastAPI, Apache Kafka
• Engineered an event-driven system using Kafka for real-time activity tracking and asynchronous
logging between services.
• Implemented producer/consumer logic to ensure system decoupling and high availability.
Fitness & Diet Tracking Ecosystem | Flutter, Python, MongoDB
• Developed a cross-platform mobile app in Flutter featuring personalized meal tracking and
calorie calculation logic.
• Built a robust CRUD API using Python and MongoDB to manage user-specific health data