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Assessing your cultural and operational fit
Aspiring Machine Learning Engineer and Software Developer with hands-on experience in AI-driven applications, deep learning, and backend development. Skilled in Python, FastAPI, computer vision, and NLP, with projects including deepfake detection and AI-powered chatbot systems. Completed an AI internship at Infosys Springboard and summer training at GRAStech, where I worked on machine learning models, forecasting systems, EDA, and deployment. Participated in the IBM Expert Labs National Hackathon as a finalist, gaining valuable teamwork and problem-solving experience. Passionate about continuous learning, full-stack AI integration, and reading novels in free time to enhance creativity and analytical thinking.
Babu Banarasi Das University
B.Tech · Computer Science & Engineering in Artificial Intelligence
September 18, 2022 – June 29, 2026
St. Therese's School
12 · PCM
March 31, 2021 – August 14, 2022
Infosys Springboard
AI Intern
October 5, 2025 – December 14, 2025
Lucknow, Uttar Pradesh, India
Agri-Yield Predictor
October 5, 2025 – December 14, 2025
During my internship at [Infosys Springboard](https://www.infosysspringboard.com/?utm_source=chatgpt.com), I worked on developing AI and machine learning solutions focused on predictive analytics and forecasting. I built a crop yield prediction system using Random Forest algorithms that achieved over 95% accuracy and implemented time-series forecasting models using Prophet with around 76% accuracy. I also integrated machine learning models with FastAPI and deployed applications using platforms like Render and Vercel, gaining practical experience in backend development, model deployment, and real-world AI application integration. https://agriyield-predictor-system.vercel.app/
View ProjectIBM Expert Lab National Hackathon
IBM
August 7, 2025 – Present
The candidate scored 0% on this technical assessment, indicating a complete lack of demonstrated proficiency in problem-solving, coding logic, technical accuracy, and practical implementation under test conditions.
Limitations
The candidate scored 0% on this technical assessment, indicating a complete lack of demonstrated proficiency in problem-solving, coding logic, technical accuracy, and practical implementation under test conditions.
Limitations
Cultural Fit Analysis
The psychometric test score of 66% suggests an average cultural fit. While the candidate demonstrates some team collaboration experience through projects and volunteer work, the scores in work attitude and stress handling indicate potential areas for development to fully align with a strong cultural fit.
Soft Skills & Operational Fit
The psychometric test indicates average performance in logical reasoning, work attitude, stress handling, and team collaboration (66%). The candidate's resume mentions 'collaborating with team' on a project and a 'Group Leader' role in NSS, suggesting foundational experience in teamwork, but the psychometric score highlights areas for development in operational effectiveness.