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AI Engineer with less than a year in Prompt Engineering & RAG
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Highly motivated and results-driven individual with a strong foundation in Computer Science and Engineering, specializing in AI and Machine Learning. Possessing practical experience as a Trainee in AI model development and contributing to large-scale AI training and evaluation workflows. Demonstrates proficiency in various AI/ML techniques, including LLMs, RAG, and Computer Vision, as evidenced by successful project implementations like an AI Coding Assistant, AI Diabetes Prediction System, and a Livestock Monitoring AI System. Published a Scopus-indexed paper on Indian language identification using ML/DL models, showcasing a strong commitment to research and innovation in the AI domain.
Lovely Professional University
Bachelor of Technology · Computer Science and Engineering
August 1, 2022 – June 30, 2026
Outlier
Trainee
January 1, 2025 – March 1, 2025
India
AI Coding Assistant (GitHub Copilot Clone)
April 1, 2026 – June 1, 2026
Developed a GitHub Copilot-style AI assistant using Ollama and open-source LLMs to generate code, explain logic, identify bugs, and provide contextual programming support. Engineered a RAG-based architecture with LangChain, embeddings, and ChromaDB to enable repository-aware code understanding and semantic search capabilities. Integrated vector search and context management pipelines to retrieve relevant code snippets and documentation, improving response accuracy and developer experience. Built scalable FastAPI APIs and a React-based user interface to deliver real-time AI-powered coding assistance, code review, and debugging support.
AI Diabetes Prediction System
January 1, 2026 – April 1, 2026
Developed a full-stack AI healthcare platform for diabetes risk prediction using machine learning models, real-time inference pipelines, and predictive analytics to support early diagnosis. Integrated LLM-powered conversational AI with Retrieval-Augmented Generation (RAG) workflows, enabling personalized healthcare assistance and context-aware medical insights. Built scalable REST APIs using Flask and optimized backend services with Docker and Docker Compose, ensuring efficient deployment, containerization, and service management. Designed interactive analytics dashboards and AI-driven user interfaces using React, Vite, and Tailwind CSS, improving accessibility, visualization, and user engagement.
FarmGuardian - Livestock Monitoring AI System
August 1, 2025 – December 1, 2025
Developed a real-time AI-powered livestock monitoring system using YOLOv8 and OpenCV for animal detection, activity tracking, and automated alert generation across multiple camera streams. Built scalable backend services with FastAPI, JWT authentication, WebSockets, and SQLModel/SQLAlchemy, improving system responsiveness and enabling secure real-time communication. Designed an interactive React dashboard (Vite + Tailwind CSS) to visualize live camera feeds, analytics, and monitoring insights with latency below 150ms. Implemented real-time event detection and alerting pipelines, enabling 24/7 automated monitoring and reducing manual supervision requirements.
Published Scopus-indexed paper on Indian language identification using ML/DL models (Naive Bayes, CNN, LSTM, HMM)
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate demonstrates a strong interest in cutting-edge AI technologies, evidenced by projects involving LLMs, RAG, and real-time computer vision. The diversity of projects (coding assistant, healthcare, livestock monitoring) shows adaptability and a broad application of AI skills, which aligns well with innovative and fast-paced environments. The academic achievement of publishing a paper further highlights a commitment to learning and contributing to the field. The candidate's focus on full-stack AI development suggests a preference for end-to-end ownership, which can be a good fit for teams seeking versatile engineers.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and a strong drive to build complete, functional systems. The internship experience at Outlier suggests an ability to contribute to large-scale AI workflows and perform quality assurance. The remote nature of the internship also implies self-discipline and effective independent work. However, without direct interview data, specific soft skills like teamwork, leadership, or stress handling cannot be fully assessed.