DEBANJANA SARKAR

AI Engineer

https://talent.gravityer.com/debanjana-sarkar

AI Engineer with 1+ years in Artificial Intelligence & Machine Learning

DGT, Microsoft and Edunet Foundation - NSTI W KOLKATA

Key Strengths

Strong interest and foundational knowledge in Artificial Intelligence and Machine Learning, evidenced by numerous personal projects.
Practical experience with Generative AI, Streamlit, and GitHub collaboration from internship.
Demonstrated ability to apply ML/NLP techniques to solve real-world problems (e.g., Parkinson's Prediction, Speech Emotion Recognition, Customer Support Ticket Classifier).
Proficiency in data analysis and visualization tools like Python, SQL, and Power BI.
Commitment to continuous learning, as shown by multiple certifications in AI, Data Analysis, and Cloud concepts.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's profile indicates a strong alignment with a learning-oriented and collaborative culture, as evidenced by their stated 'willingness to learn' and 'strong teamwork and collaboration abilities'. The diverse personal projects, ranging from environmental impact estimation to healthcare predictions, suggest a broad interest in applying AI for societal benefit, which aligns with a purpose-driven organizational culture. The pursuit of numerous certifications from Microsoft and LinkedIn also shows a proactive approach to skill development and staying current with industry trends. The future-dated internship, while not current experience, shows a planned engagement with industry leaders like Microsoft, indicating ambition and a desire for structured professional growth.

Soft Skills & Operational Fit

The candidate demonstrates a 'willing to learn' attitude and mentions 'strong teamwork and collaboration abilities' in their skills section. Project descriptions are clear, indicating good communication of technical concepts. The diverse range of personal projects suggests initiative and problem-solving orientation. The internship experience, though future-dated, highlights exposure to a structured learning environment and end-to-end AI solution development.

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Projects

Bank Loan Approval Analysis

June 1, 2026 – Present

Developed an interactive ML-driven dashboard using Streamlit and Cosine Similarity to provide bank Loan Approval Analysis. The system features a custom UI with Plotly visualizations for real-time trend analysis and automated Excel report generation.

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Customer Support Ticket Classifier

June 1, 2026 – Present

Customer Support Ticket Classifier is a Natural Language Processing (NLP) and Machine Learning-based project designed to automatically classify customer support tickets into different categories such as billing, technical issue, account problem, complaint, feedback, and general inquiry. It helps organizations manage customer requests efficiently by reducing manual effort and improving response time.

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Sales Performance Dashboard

June 1, 2026 – Present

Sales Performance Dashboard is a data analytics and visualization project designed to monitor, analyze, and improve business sales performance. It helps organizations track important sales metrics such as total revenue, profit, monthly sales growth, top-selling products, regional performance, and customer trends in a clear and interactive dashboard.

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Plastic Type Identification System

June 1, 2026 – Present

With the increasing global concerns regarding plastic pollution, efficient plastic waste management has become a pressing issue. A critical aspect of recycling is identifying the type of plastic to ensure proper sorting, which greatly enhances the recycling process and reduces contamination of recyclable materials.

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Public Transit Carbon Offset Estimator

June 1, 2026 – Present

Highlight Public Transit Carbon Offset Estimator by scraping a public database to generate greener commuting by quantifying the CO2 emissions saved when taking public transit instead of driving a single-occupancy vehicle for a specific distance.

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AI Driver Drowsiness Detection

June 1, 2026 – Present

The system monitors a driver's eye movements and facial behavior in real time using a webcam. When signs of drowsiness are detected, the application immediately triggers an alert to help the driver stay attentive.

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Parkinson Syndrome Prediction

June 1, 2026 – Present

Parkinson Syndrome Prediction is a machine learning-based healthcare project designed to predict the likelihood of Parkinson's disease using patient medical data, voice measurements, or motor symptom indicators. Parkinson's disease is a progressive neurological disorder that affects movement, balance, and coordination. Early detection can help patients receive timely treatment and improve quality of life.

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Speech Emotion Recognition

June 1, 2026 – Present

Speech Emotion Recognition is an Artificial Intelligence and Machine Learning-based project designed to identify human emotions from voice signals. The system analyzes speech patterns, tone, pitch, frequency, energy, and other audio features to detect emotions such as happy, sad, angry, neutral, fear, and surprise. This technology helps computers understand human feelings through speech and improves human-computer interaction.

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