AKSHAY CHOUDHARY
AI EngineerAI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with less than a year in Deep Learning, GenAI, and Python development.
Akshay Choudhary is a passionate technologist and researcher with a strong foundation in Biotechnology and a keen interest in Artificial Intelligence and Machine Learning. Currently, a Research Intern at IIT Kharagpur, he is actively involved in reproducing and extending deep learning architectures for enzyme kinetic prediction and building robust PyTorch-based training pipelines. With hands-on experience in GenAI, Deep Learning frameworks like PyTorch, and Python, complemented by open-source contributions to PyCQA/Pylint, Akshay demonstrates a commitment to advancing AI research and developing impactful applications.
National Institute of Technology, Rourkela
Bachelor of Technology · Biotechnology
August 1, 2023 – Present
IIT Kharagpur
Research Intern
May 1, 2026 – Present
Raurkela, Odisha, India
DocMind – Multi-PDF RAG System
June 1, 2026 – Present
Built a full-stack Retrieval-Augmented Generation (RAG) platform enabling users to upload multiple PDFs and interact with documents through natural language conversations. Implemented semantic retrieval pipelines using embeddings, vector search, document chunking, and conversational memory for context-aware question answering. Developed citation-aware responses, real-time streaming APIs, and scalable document ingestion workflows using FastAPI and ChromaDB.
View ProjectDeep Learning for Enzyme Kinetics Prediction
June 1, 2026 – Present
Reproduced the DLKcat architecture for enzyme turnover number (kcat) prediction using protein sequence and molecular graph representations. Built deep learning pipelines combining graph neural networks and sequence encoders for biochemical property prediction. Achieved baseline performance of R2 = 0.46, RMSE = 3.67, and MAE = 2.72 on benchmark datasets.
View ProjectMiniML - Machine Learning Library
June 1, 2026 – Present
Developed and published an open-source machine learning library implementing Linear Regression, KNN, K-Means, DBSCAN, PCA, and neural network components. Designed Scikit-learn-inspired APIs supporting model training, preprocessing, evaluation, and inference workflows. Built modular architecture with automated testing, package versioning, and CI/CD pipelines using GitHub Actions.
View ProjectTop 10 Finalist - Teach-a-thon Hackathon (500+ teams)
Unknown
June 1, 2026 – Present
Codeforces (Max Rating: 922)
Codeforces
June 1, 2026 – Present
CodeChef 3-Star (Max Rating: 1346)
CodeChef
June 1, 2026 – Present
Contributed multiple merged pull requests to PyCQA/Pylint, a widely used open-source Python static analysis project.
PyCQA/Pylint
June 1, 2026 – Present
Organized HackOdisha 4.0, a 36-hour national hackathon involving 100+ participating teams.
HackOdisha
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, including a full-stack RAG system, a deep learning research project, and an open-source ML library, indicates a broad interest in AI/ML applications and a proactive approach to learning. Their open-source contributions and hackathon participation suggest a collaborative spirit and engagement with the developer community. The target role of AI Engineer aligns well with their demonstrated technical skills and project focus.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and problem-solving skills through their open-source contributions and personal projects. Their involvement in organizing a hackathon suggests leadership and organizational capabilities. The detailed project descriptions indicate good communication of technical concepts. The candidate's current research intern role at IIT Kharagpur aligns well with a continuous learning mindset.