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AI Engineer with less than a year in Computer Vision & NLP
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Harshit Sojitra is an aspiring AI Engineer with a strong academic background, currently pursuing an M.Tech in Computer Science & Engineering. With practical experience as a Web Development Intern and hands-on exposure to AI/ML, Deep Learning, and Generative AI, Harshit demonstrates proficiency in Python, C++, and various frameworks like PyTorch and TensorFlow. Their project portfolio highlights work in semantic communication with Generative AI, underwater image enhancement, and LLM-based ranking systems, showcasing a keen interest in developing innovative AI solutions.
Indian Institute of Information Technology, Vadodara
M.Tech · Computer Science & Engineering
August 1, 2025 – August 1, 2025
Shantilal Shah Engineering College, Bhavnagar
B.Tech · Information Technology
July 1, 2021 – May 1, 2025
Shri GyanBharti School, Junagadh
12th
June 1, 2020 – May 1, 2021
Shree Swaminarayan Gurukul, Visavadar
10th
July 1, 2018 – May 1, 2019
Virtual Rudra
Web Development Intern
January 1, 2025 – May 1, 2025
India
Semantic Communication with Generative AI
June 1, 2026 – Present
• An Image Caption Neural Network translates raw visual data into text, allowing a Large Language Model to assign contextual importance scores that follow adaptive, error-resilient transmission over noisy channels. • BERT mathematically predicts and repairs any corrupted text, which a Generative AI model then utilizes to synthesize a high-quality reconstruction of the original image.
Multi-Scale Transformer-Based Underwater Image Enhancement
June 1, 2026 – Present
• Designed a CNN encoder-decoder multi-scale transformer network with cross-attention for enhancing degraded underwater images. • Applied feature fusion (FSM) and residual learning to improve color correction and image clarity.
Generative AI for Text-to-Image Generation with Trajectory Control
June 1, 2026 – Present
• Investigated training-free layout control in Stable Diffusion, evaluating trajectory-conditioned image generation methodologies against traditional mask and box-based baselines. • Optimized spatial conditioning in pre-trained generative models by manipulating text-related cross-attention layers without requiring additional specialized training pipelines.
LLM-based Full Ranking System for Passage Retrieval
June 1, 2026 – Present
• Built a long-context LLM-based passage ranking system, replacing sliding-window methods with full-ranking to improve efficiency and ranking accuracy. • In depicted importance-aware loss and multi-pass label generation, we achieve better NDCG performance and reduced latency.
Fundamentals of Deep Learning
NVIDIA
October 1, 2025 – Present
Cultural Fit Analysis
The candidate's project portfolio is highly focused on advanced AI/ML research, demonstrating a strong passion and deep interest in the field. This aligns well with a culture that values innovation and technical depth in AI. The academic background and project diversity suggest a proactive learning attitude. However, the lack of significant industry experience beyond a web development internship means there's limited data to assess adaptability to corporate environments or cross-functional team collaboration.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, research-oriented problems, suggesting strong problem-solving and analytical skills. The academic nature of most projects implies a capacity for independent research and learning. However, there is limited evidence of collaborative work or communication skills in a professional team setting, which are crucial for operational fit.