Help & Supportcareers@gravityer.com
AI Engineer with less than a year in Machine Learning, Deep Learning, and Business Intelligence.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
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.
GenAI Job Simulation
BCG (Forage)
January 1, 2025 – Present
Data Analytics Job Simulation
Deloitte Australia (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.