Amna Rafi

AI Engineer and Backend Developer with 2+ years of hands-on experience building production-grade Python applications, REST APIs, LLM applications, RAG pipelines, and conversational AI systems. Skilled in Python, FastAPI, Django, LangChain, LangGraph, Qdrant, pgvector, and OpenAI APIs. Experienced with embedding models, semantic search, intent detection, multi-agent orchestration, and cloud deployment on AWS.

Published IEEE researcher with a proven track record of delivering scalable, secure, and user-focused AI solutions.

AI Engineer at CodAgentic (2024-10 – 2026-02)

• Engineered production-grade LLM applications, backend services, and agentic workflows using Python, FastAPI, LangChain, and LangGraph; reduced manual processing overhead by automating AI-driven pipelines end-to-end.
• Designed and deployed scalable REST APIs, chatbot platforms, RAG systems, and multi-agent orchestration workflows serving reliable performance across remote production environments.
• Built and managed vector database collections using Qdrant and pgvector; implemented embedding models and semantic search for knowledge-base and FAQ automation systems.
• Developed LLM-based intent detection, response ranking, and fallback systems, improving conversational AI quality and automating customer service workflows at scale.
• Integrated AI services into AWS cloud environments and multi-tenant architectures, supporting real-time deployment and MLOps retraining pipelines.

Full Stack Developer Intern at Tech and Sols (2025-06 – 2025-09)

• Developed and maintained full-stack web applications including LMS platforms and workflow automation tools, measurably improving internal operational efficiency.
• Built responsive React.js frontends and scalable Django/FastAPI backend services following API design, debugging, version control, and CI/CD best practices.

AI Developer Intern at Ownova (2025-03 – 2025-05)

• Developed AI chatbots, voice agents, and RPA bots across multiple platforms using LLM APIs (OpenAI, Ollama), prompt engineering, and automation workflows.
• Improved conversational AI quality through structured prompts, embedding-based retrieval, reusable workflow design, response evaluation, and user-focused iteration.

Bachelor of Information Technology in Information Technology – University of Management and Technology (UMT)