AI Engineer with less than a year in Machine Learning & Data Science.
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AI & Data Science engineering graduate with proven, hands-on experience building and deploying end-to-end machine learning systems across healthcare, finance, and e-commerce domains. Proficient in the complete ML lifecycle: data ingestion, ETL pipelines, exploratory analysis, feature engineering, model training (XGBoost, CatBoost, Random Forest, CNN), evaluation, and production deployment via Flask REST APIs on cloud platforms. Practical exposure to Deep Learning, NLP, Transformer-based LLMs, Retrieval-Augmented Generation (RAG), computer vision (YOLOv8, SegFormer), and Explainable AI (SHAP). Committed to building scalable, interpretable AI products that solve real business problems.
Dr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow, UP, India
B.Tech · Artificial Intelligence & Data Science
August 1, 2022 – May 1, 2026
CODETECH IT SOLUTIONS
Machine Learning Intern
September 1, 2025 – November 1, 2025
India
AI Financial Intelligence & Risk Detection System
June 6, 2026 – Present
• Designed a multi-model AI analytics platform to assess financial health and detect risk from structured user financial data covering income, expenses, loans, and spending behaviour. • Engineered 27 domain-specific features (debt-to-income ratios, spending velocity, trend indicators) and trained three specialized models: Random Forest for credit risk classification, XGBoost for 5-step expense forecasting, and Isolation Forest for fraud and anomaly detection. • Integrated SHAP (SHapley Additive Explanations) for model interpretability and transparency, delivering explainable AI-driven financial recommendations aligned with compliance expectations. • Deployed a scenario-based what-if analysis engine and interactive Chart.js dashboard via Flask REST API, serving personalized financial decision support to end-users in real time.
View ProjectReal-Time Order Book ML Trading Signal System
June 6, 2026 – Present
• Built a high-frequency ML pipeline using XGBoost to predict next-minute equity price direction (up/down) from real-time Level-2 order book data streamed via WebSocket API. • Engineered market microstructure features including bid-ask spread, order book imbalance, mid-price velocity, and volume delta capturing short-term directional signals unavailable in standard OHLCV data. • Developed a live frontend interface rendering real-time predictions and market signal indicators, demonstrating applied ML in a quantitative finance context.
View ProjectCKD Clinical Risk Detection System
June 6, 2026 – Present
• Developed a clinical decision-support ML system using CatBoost, achieving 98.99% accuracy on early-stage Chronic Kidney Disease detection from structured patient biomarker data. • Performed rigorous feature engineering on clinical variables, managed class imbalance, and validated model reliability using Precision, Recall, F1-score, and ROC-AUC across cross-validation folds. • Deployed a secure Flask REST API web application serving real-time CKD risk predictions, designed as a scalable proof-of-concept for integration into hospital information management systems.
View ProjectIntroduction to Generative AI
Google Cloud
June 1, 2026 – Present
Data Science
HP LIFE
June 1, 2026 – Present
Machine Learning
CloudyML
June 1, 2026 – Present
Git & GitHub
CloudyML
June 1, 2026 – Present
Prompt Design in Vertex AI
Google Cloud
June 1, 2026 – Present
Python for Data Science
CloudyML
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity (finance, healthcare, trading) and interest in Applied AI Research, GenAI Product Development, Healthcare & Fintech AI Systems align well with a dynamic, innovation-focused environment. The self-driven nature evident in personal projects and certifications suggests a proactive learner. The remote internship experience indicates an ability to work independently and as part of a distributed team. The candidate's stated availability for relocation to India, Middle East, and Europe, along with an immediate joining preference, shows flexibility and commitment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and problem-solving approach, with an emphasis on building practical, deployable solutions. The internship experience highlights collaboration and documentation skills. The diverse project portfolio suggests adaptability and a strong interest in applying AI to real-world problems. However, without direct interview data, a deeper assessment of communication under pressure, teamwork dynamics, and specific problem-solving methodologies is limited.