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AI Engineer with 2+ years in building and deploying ML models and deep learning solutions.
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Artificial Intelligence & Machine Learning Engineer with 2+ years of hands-on experience in building and deploying ML models, deep learning solutions, and data-driven applications. Skilled in Python, Scikit-learn, TensorFlow, Keras, Pandas, NumPy, and SQL, with practical exposure to Natural Language Processing (NLP), supervised and unsupervised learning, and data preprocessing and visualization. Experienced in working on real-world AI/ML projects from data collection through model evaluation and basic deployment. Passionate about solving business problems using AI, continuously learning new technologies, and contributing to impactful, data-driven projects in collaborative team environments.
Jayawantrao Sawant College Of Engineering Pune
BE · Information Technology
N/A – June 30, 2024
SDK Infotech Pvt Ltd
AI/ML Engineer
January 1, 2024 – Present
India
Customer Churn Prediction System Using Machine Learning
June 18, 2026 – Present
Developed a complete end-to-end Machine Learning pipeline to predict customer churn for a subscription-based business, enabling the retention team to proactively identify and engage at-risk customers before they leave. Collected and integrated customer data from a MySQL database covering demographics, subscription details, usage behavior, and payment history – consolidating over 20,000+ customer records into a single, clean analytical dataset using Python and SQL queries. Executed detailed data preprocessing — handled missing values using median imputation, applied one-hot encoding for categorical features, used StandardScaler for feature scaling, and addressed class imbalance using SMOTE (Synthetic Minority Oversampling Technique) to produce a balanced, model-ready dataset. Performed hyperparameter tuning using GridSearchCV on the XGBoost model, optimizing parameters including n_estimators, max_depth, and learning_rate, which improved F1-Score by 8% over the baseline configuration. Analyzed feature importance scores from the trained model to identify and rank the top 10 factors driving customer churn, providing the business team with clear, data-backed reasons behind predictions for actionable retention planning. Deployed the trained model as a REST API using Flask, allowing the internal team to input new customer data and receive real-time churn probability scores for decision making without requiring technical knowledge of the underlying ML model. Built an interactive Power BI dashboard displaying churn distribution by contract type, tenure band, and monthly spend – giving management a clear, visual understanding of churn patterns and helping prioritize customer retention efforts. Documented the complete project workflow, model performance metrics, and business recommendations in a structured report presented to the project supervisor, demonstrating strong communication and data storytelling skills and highlighting a projected 12–15% reduction in churn rate upon strategy implementation.
Cultural Fit Analysis
The candidate's project diversity, focusing on a customer churn prediction system, shows an application of AI/ML to a practical business problem. Their stated passion for solving business problems using AI and continuous learning indicates a proactive and growth-oriented mindset. The role alignment with 'AI Engineer' is strong given their current and project experience. The breadth of skills across ML, DL, NLP, and data visualization suggests adaptability and a willingness to tackle various challenges, contributing positively to team dynamics.
Soft Skills & Operational Fit
The candidate demonstrates good communication skills through detailed project descriptions and experience in presenting findings. Their project work indicates a structured approach to problem-solving and an ability to work on real-world business problems. The emphasis on documentation and version control (Git/GitHub) suggests an understanding of collaborative development practices. The candidate's passion for continuous learning and solving business problems using AI aligns well with a dynamic technical environment.