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AI Engineer with less than a year in Machine Learning, Deep Learning, and Cloud Platforms
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Assessing your cultural and operational fit
Highly motivated and skilled individual pursuing a PG-Diploma in Big Data Analytics with a strong foundation in Python programming, data structures, and problem-solving. Possesses expertise in AI concepts, machine learning, deep learning, and cloud computing, demonstrated through certifications in AWS and Oracle. Proven ability to develop scalable solutions and implement complex algorithms, as showcased in projects involving weather prediction, music recommendation, and anti-money laundering systems. Eager to contribute to innovative engineering roles leveraging AI and data science skills.
Centre for Development Advanced Computing
PG - Diploma · Big Data Analytics
August 1, 2025 – June 30, 2026
Acropolis Institute of Technology and Research
B.Tech · Computer Science and Engineering
August 1, 2020 – June 30, 2024
SVM High School
12th Grade
June 1, 2018 – May 31, 2020
SVM High School
10th Grade
June 1, 2016 – May 31, 2018
Anti-Money Laundering System (Ongoing)
January 1, 2026 – March 1, 2026
Built an AML system to detect suspicious transactions and financial fraud patterns in real-time banking environments. Applied rule-based detection and Isolation Forest for anomaly identification in high-volume financial transaction datasets. Integrated RAG pipeline for AML regulations and compliance policy retrieval with enhanced contextual intelligence. Automated explainable SAR report generation for regulatory compliance reporting and comprehensive audit readiness.
Music Recommendation System
September 1, 2025 – November 1, 2025
Built a scalable recommendation system using Spark and ML algorithms for highly personalized music recommendations. Implemented clustering and similarity-based filtering to significantly improve overall user recommendation precision rates. Processed large datasets efficiently using Hadoop HDFS and PySpark frameworks for scalable distributed data processing. Applied preprocessing, feature engineering, and evaluation techniques for improved overall system performance and accuracy.
Weather Prediction using CNN
April 1, 2025 – July 1, 2025
Built a weather image classification system using CNN, ResNet, and Inception architectures for accurate prediction. Implemented pooling, dropout, and batch normalization to improve overall model performance and reduce overfitting. Performed image preprocessing and data augmentation to enhance dataset quality and overall prediction accuracy. Evaluated models using K-fold cross-validation and standard classification metrics to ensure performance and accuracy.
Certification of AWS for Generative AI Foundations
AWS
February 1, 2026 – Present
Certification of AWS for Data Engineering
AWS
January 1, 2026 – Present
Oracle Certified Foundations Associate AI Foundations
Oracle
October 1, 2025 – Present
Certification of AWS for Cloud Foundations
AWS
September 1, 2025 – Present
Certification of MPSSDEGB-FITT for AI Builder
MPSSDEGB-FITT
March 1, 2024 – Present
Certification of UDEMY for Python Programming
UDEMY
April 1, 2021 – Present
Cultural Fit Analysis
The candidate shows a strong interest in AI technologies and continuous learning, as evidenced by multiple certifications and academic projects. The diversity of projects (image classification, recommendation systems, fraud detection) indicates a broad interest in applying AI to different problem domains. The pursuit of a PG Diploma in Big Data Analytics further aligns with roles requiring robust data processing capabilities. However, all projects are academic, and there is no professional experience to assess cultural fit in a corporate setting.
Soft Skills & Operational Fit
The candidate's extracurricular activities, such as participating in a marathon and serving as a class representative, suggest teamwork, leadership, and communication skills. However, without direct work experience, the operational fit in a professional AI engineering environment, including stress handling and collaboration, is not explicitly demonstrated.