AI Engineer with less than a year in Machine Learning, Deep Learning, and Business Intelligence.
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AI & Data Science engineer (CGPA: 8.85, KSCST-funded final-year project) combining deep expertise in Machine Learning, Deep Learning, and Business Intelligence to convert raw data into actionable decisions. Architected and shipped end-to-end ML pipelines from KNN imputation and Isolation Forest outlier detection to Xception-based deepfake detection, attaining ~94% F1-score plus 5+ Power BI dashboards that slashed reporting lag by 2 days. Fluent in Python, SQL, TensorFlow, Scikit-learn, Power BI, and Git; familiar with Agile workflows and REST API deployment via Flask/FastAPI. Seeking entry-level roles in Data Analytics, Business Analytics, Data Science, or Machine Learning Engineering.
Bearys Institute of Technology, Mangalore
B.E. · Artificial Intelligence & Data Science
August 1, 2022 – June 30, 2026
GHSS Bekur, Kasaragod
Pre-University (PCMB)
June 1, 2020 – May 31, 2022
Cognifyz Technologies
Power BI Intern – Business Intelligence & Reporting
March 1, 2026 – April 1, 2026
India
VisionAstraa EV Academy
AI/ML Intern – Electric Vehicle Data Analytics
February 1, 2026 – May 1, 2026
India
EV Battery State-of-Health (SOH) Prediction System
February 1, 2026 – May 1, 2026
Constructed an end-to-end ML pipeline on commercial EV bus battery data (662 records): automated CSV ingestion, KNN imputation for missing values, Isolation Forest anomaly filtering, and standard feature scaling via Scikit-learn. Fitted and benchmarked Random Forest and Linear Regression models; Random Forest attained MSE: 0.0017 and R²: 0.53 — demonstrating reliable SOH inference with limited features (SOH(OCV), mileage). Released as an interactive Streamlit web app enabling real-time battery health predictions; applicable to predictive maintenance, fleet management, and smart energy systems.
View ProjectAI DeepForge: Multimodal Forensic System for AI-Generated Content Detection
January 1, 2025 – January 1, 2026
Conceptualised and built a multimodal deepfake and synthetic-content detection platform covering text, image, audio, and video — targeting misinformation detection for media and law-enforcement agencies; attained ~94% F1-score across all four modalities. Fine-tuned an Xception-based CNN on FaceForensics++ video frames; fitted NLP classifiers (TF-IDF + transformer embeddings) for AI-text detection; integrated Librosa-based audio analysis — all unified under a single Flask REST API with FastAPI-compatible architecture. Steered end-to-end solo development: model architecture, data pipeline, Flask deployment, and UI — first open-source tool to simultaneously scrutinise four media types for digital forensics use cases. Secured KSCST Student Project Programme (SPP) 2025-26 government funding, validating real-world impact in digital forensics.
View ProjectAI-Powered Phishing Detection System
January 1, 2025 – January 1, 2026
Developed a Random Forest classifier on labelled phishing email data using TF-IDF n-gram features, URL entropy, and sender domain reputation scores — attaining 96% accuracy, 8% above Naïve Bayes baseline. Utilised full NLP preprocessing (tokenisation, stemming, stopword removal) and feature engineering to construct a 50+ feature matrix; rigorously assessed performance via precision, recall, F1-score, and ROC-AUC metrics. Structured for REST API microservice deployment via Flask, exposing a real-time classification endpoint for email gateway integration.
Data Analytics Job Simulation
Deloitte Australia (Forage)
January 1, 2025 – Present
GenAI Job Simulation
BCG (Forage)
January 1, 2025 – Present
SQL Analytics and BI on Databricks
Simplilearn SkillUp
January 1, 2025 – Present
Innovation & Design Thinking
COMEDKARES Innovation Hub
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from multimodal forensic systems to EV battery prediction and phishing detection, indicates a broad interest and adaptability within the AI/ML domain. Their academic background in AI & Data Science, coupled with internships in both AI/ML and Business Intelligence, shows a versatile skill set. The KSCST funding and 'Best Speaker' award suggest initiative and a proactive approach, aligning well with a culture that values innovation and communication. The target role of 'AI Engineer' aligns well with their project experience in model architecture, data pipelines, and deployment.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical thinking, problem-solving, and stakeholder communication skills through project descriptions and internship experiences. Their involvement in team-based projects and presentations to senior engineers suggests good collaboration and communication within an operational context. The mention of Agile sprint cycles indicates familiarity with structured development methodologies.