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AI Engineer with less than a year in LLM and RAG Systems
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Assessing your cultural and operational fit
AI/LLM Engineering Intern with expertise in building AI-powered platforms, RAG pipelines, and prompt engineering. Experienced in developing CNN-based image classification models and multi-agent clinical decision support systems. Proficient in Python, TensorFlow/Keras, and FastAPI, with a strong foundation in deep learning and natural language processing.
Institute of Infrastructure Technology Research and Management
B.Tech · Computer Science
August 1, 2023 – June 30, 2027
QGenii AI
AI/LLM Engineering Intern
January 1, 2026 – Present
India
Vectorless RAG – Reasoning-Driven Document QA System
March 1, 2026 – March 1, 2026
Engineered a vector-free Retrieval-Augmented Generation (RAG) system using LLM reasoning to navigate documents without embeddings or vector databases. Designed a hierarchical PageIndex architecture for intelligent document traversal and context-aware retrieval similar to human reading. Built a FastAPI backend for PDF ingestion and querying, integrating Mistral-7B via Hugging Face to generate answers with transparent reasoning traces for explainable AI.
View ProjectClinIQ AI Clinical Decision Support Agent
March 1, 2026 – March 1, 2026
Built a multi-agent clinical decision support system using LangGraph orchestration and RAG over verified WHO/NIH medical guidelines with explainable confidence scoring. Designed 5 specialized agents for input parsing, document retrieval, diagnosis reasoning, drug interaction flagging, and next-step recommendations with source citations. Integrated a session-based RAG medical chatbot with citation-grounded responses and stateless conversation memory using a floating chat widget.
View ProjectPotato Disease Detection (Deep Learning)
February 1, 2025 – February 1, 2025
Developed a CNN-based image classification model to detect potato leaf diseases across Healthy, Early Blight, and Late Blight categories. Performed data preprocessing, augmentation, normalization, and trained the model with evaluation and visualization using confusion matrices and learning curves. Built an end-to-end pipeline and integrated the trained model into a web interface for real-time disease prediction from uploaded leaf images.
View ProjectComplete Data Science, Machine Learning, Deep Learning & NLP Bootcamp
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects show a diverse interest in AI applications, from medical decision support to agricultural disease detection and novel RAG architectures. This breadth of interest, combined with an internship focused on AI-powered platforms, aligns well with the innovative and problem-solving culture often found in AI engineering teams. The focus on explainable AI and robust system design in projects indicates a thoughtful approach to AI development. The candidate is still early in their career (experience level 0), which suggests a high potential for growth and learning within a supportive team environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project work and a stated achievement of solving 300+ problems. The remote internship indicates adaptability and self-management. The project descriptions suggest an ability to work on complex, multi-faceted problems, which is crucial for an AI Engineer role. However, without specific psychometric test results, a deeper assessment of work attitude, stress handling, and team collaboration is not possible.