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AI Engineer with less than a year in Deep Learning, Computer Vision, and Generative AI
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Final-year B.Tech Computer Science Engineering (Artificial intelligence) student skilled in Deep Learning, Computer Vision, and Generative AI. Developed high-accuracy models and RAG-based QA systems. Interested in scalable AI solutions and ML model deployment.
Chhattisgarh Swami Vivekanand Technical University
B.Tech (Hons.) · Computer Science & Engineering (Artificial Intelligence)
August 1, 2022 – June 30, 2026
National Institute of Technology Karnataka (NITK)
ML Research Intern
January 1, 2025 – July 1, 2025
Mangalore, Karnataka, India
Multi-Organ Cancer Grading using Custom CNN (Kidney & Liver Histopathology)
May 18, 2026 – Present
Developed a unified custom CNN with SE, RFB, RCAB, RPAB, and MKRC blocks for cancer grading. Trained on 5-class Kidney dataset and evaluated on 3-class Liver dataset to test cross-dataset generalization. Achieved 93.56% accuracy (Kidney) and 98.2% accuracy (Liver), outperforming RCCGNet and LiverNet.
View ProjectMultimodal Emotion Recognition using Audio & Text
May 18, 2026 – Present
Built an end-to-end deep learning system to detect human emotions from both speech and textual inputs on the TESS dataset. Designed a unified model pipeline that extracts audio and text features and combines them for final emotion prediction. Achieved 99% accuracy (Audio), 100% accuracy (Text), and 100% accuracy (Fusion), showing improved performance using multimodal learning.
View ProjectDocument-Based Question Answering using Retrieval-Augmented Generation (RAG)
May 18, 2026 – Present
Designed and implemented a scalable end-to-end Retrieval-Augmented Generation (RAG) pipeline for document-based question answering using semantic retrieval and LLM-based generation. Built a PDF processing and semantic search pipeline (text cleaning, chunking, cosine similarity) to enable real-time document querying, improving answer accuracy and reducing hallucination.
View ProjectCultural Fit Analysis
The candidate's profile, characterized by diverse academic projects and a research internship in AI, strongly aligns with a culture of innovation, continuous learning, and problem-solving. Their proactive engagement in cutting-edge AI domains like RAG and medical imaging suggests a passion for impactful technological advancements and a drive to push boundaries, which is highly compatible with a forward-thinking tech environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving abilities through the design of custom AI architectures and complex pipelines. Their focus on improving performance, generalization, and model interpretability, particularly in medical imaging, indicates a meticulous and quality-driven approach. The ability to manage end-to-end projects from data preprocessing to model evaluation highlights strong operational capabilities and initiative.