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AI Engineer with less than a year in Machine Learning & Deep Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Driven Computer Science graduate and Data Science professional with practical experience in implementing Machine Learning and Deep Learning frameworks. Proficient in the complete model lifecycle-from EDA and Feature Engineering to rigorous Predictive Modeling and evaluation using large-scale, real-world datasets. Proven ability to architect end-to-end ML Pipelines and design high-impact Power BI dashboards to transform complex data into strategic solutions.
MG University (SS College, Poothotta, Kerala)
Bachelor of Science · Computer Science
January 1, 2022 – January 1, 2025
Luminar Technolab
Data Science Intern (Python, ML, Al, Power BI)
July 1, 2025 – February 28, 2026
India
AI-Powered EQ Assessment System
June 1, 2026 – Present
Built end-to-end EQ assessment web app using Python OOP, Streamlit, and Hugging Face Transformers pipeline. Applied transfer learning with DistilBERT for real-time sentiment analysis of workplace scenario responses. Engineered scoring algorithm using sentiment polarity and confidence to generate EQ ratings (0-100) across 4 categories. Implemented input validation, st.cache_resource for model optimisation, and interactive bar chart visualisation. Identified domain mismatch limitation; proposed fine-tuning on EQ-labelled dataset as improvement.
AI Delivery Classifier & No-Ball Detector
June 1, 2026 – Present
Developed a real-time computer vision system using Python, OpenCV, and MediaPipe to analyze cricket bowling biomechanics and hand orientations. Implemented state-machine logic to classify deliveries (Off-spin, Leg-spin, Inswing, Outswing) based on precise finger-grip and palm positioning. Engineered an automated umpiring feature that calculates elbow extension angles to detect illegal actions (ICC 15-degree rule) with instant "NO-BALL” alerts. Integrated result persistence (latching) and interactive hand-gesture controls to ensure a seamless, low-latency user experience during live analysis.
IPL Atmosphere Effect: Audience Preference Study
June 1, 2026 – Present
Developed a Machine Learning model using Scikit-learn to analyze stadium attendance with 85%+ accuracy. Executed advanced data preprocessing and Feature Engineering using Python and Pandas, including outlier treatment and normalization. Implemented the Random Forest algorithm to identify key variables affecting audience preference. Deployed the predictive model as a Streamlit web application for real-time inference using Python.
Data Science and Python
National Council for Technology and Training
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest and initiative in AI/ML, aligning well with an AI Engineer role. The diversity of projects, covering NLP, computer vision, and traditional ML, indicates a broad technical curiosity. However, all projects are personal, and the only professional experience is a short internship, which limits the assessment of collaboration in a team setting. The education is ongoing, and the experience level is 0, suggesting a foundational stage in their career. The psychometric test score being 0 provides no insight into cultural fit aspects like work attitude or team collaboration.
Soft Skills & Operational Fit
The candidate's resume highlights soft skills such as Leadership, Analytical Thinking, Problem Solving, Fast Learner, Adaptability, and Technical Documentation. The project descriptions indicate an ability to identify limitations (domain mismatch) and propose improvements, suggesting analytical and problem-solving capabilities. The internship experience, though brief, shows exposure to a structured learning environment. However, the psychometric test score is 0, which is insufficient to assess work attitude, stress handling, or team collaboration effectively. The English test score of 52 suggests potential areas for improvement in communication clarity, which is crucial for operational fit.