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AI Engineer with less than a year in Machine Learning & Generative AI
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Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI-focused graduate with hands-on experience in Machine Learning, Deep Learning, and Generative AI. Built intelligent applications using RAG, LangChain, MiniLM, CNN, and YOLOv8 for conversational AI, medical question answering, and real-time fire detection systems.
TKR College of Engineering and Technology, Hyderabad
Bachelor of Technology (B.Tech) · Data Science
January 1, 2021 – January 1, 2025
Conversational RAG + CRAG Interview Assistant
January 1, 2024 – January 1, 2025
Built an end-to-end multi-turn AI assistant using OpenAI, LangChain, Pinecone, and Telegram. Implemented vector retrieval with Corrective RAG (CRAG) confidence checks for context-aware outputs. Designed memory-aware prompts to improve conversational continuity and user context retention. Added low-confidence fallback retrieval and prompt optimization workflows. Improved response accuracy and contextual relevance through iterative prompt testing and evaluation.
Fire Accident Detection System using YOLOv8 and CNN
July 1, 2023 – June 1, 2024
Developed a real-time fire and smoke detection system using YOLOv8 and Convolutional Neural Networks (CNN). Collected, cleaned, and preprocessed image and video datasets for model training. Applied image augmentation and feature extraction techniques to improve model performance. Evaluated model accuracy using Precision, Recall, F1-Score, and mAP metrics. Integrated automated alert generation for rapid emergency response. Achieved high detection accuracy with low inference latency for real-time deployment.
Personalized Financial Advisor Chatbot (LLM + RAG)
January 1, 2023 – December 1, 2023
Built an AI-powered financial advisor chatbot using LLM APIs and Retrieval-Augmented Generation (RAG). Designed data pipelines for contextual financial knowledge retrieval. Improved response relevance through prompt optimization and retrieval enhancements. Generated personalized financial insights using context-aware responses.
Medical Question Answering Assistant - MiniLM
July 1, 2022 – June 1, 2023
Built a medical question-answering system using MiniLM transformer models. Developed retrieval pipelines for domain-specific medical datasets. Implemented semantic search and contextual response generation. Achieved 82% accuracy on medical question-answering tasks.
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and focus on AI/ML, particularly in Generative AI and computer vision, which aligns well with an AI Engineer role. The diversity of academic projects (fire detection, medical Q&A, financial chatbot, interview assistant) shows a broad application interest. However, the lack of professional experience or team-based project descriptions limits the assessment of cultural fit in a collaborative, industry setting.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems and iterate on solutions (e.g., 'iterative prompt testing and evaluation'). The academic nature of projects suggests a learning-oriented mindset. However, without specific psychometric or English test scores, it's difficult to assess communication clarity, logical reasoning, work attitude, stress handling, or team collaboration in an operational context.