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AI/ML Engineer with less than a year in NLP & Data Processing
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Data Analyst and Computer Science student with proven experience processing multilingual datasets and building ML pipelines using Python and SQL. Demonstrated ability to streamline data preprocessing and enhance model accuracy while collaborating cross-functionally. Eager to apply linguistic processing skills and experiment design expertise to support language data systems and NLP advancements.
PRIST University
Bachelor of Technology · Computer Science and Engineering
November 1, 2022 – December 1, 2026
QSecuring Technologies
Technical Content, SEO & Social Media Marketing Intern
November 1, 2025 – February 1, 2026
New Delhi, Delhi, India
Viswam.AI
AI Developer Intern
May 1, 2024 – July 1, 2024
Hyderābād, Telangana, India
Indian Financial Market Analysis (India-Fi)
June 23, 2026 – Present
Developed an AI-powered web application for Indian financial news sentiment analysis using BERT-base-uncased. Processed 10,000+ financial news articles and social media posts, achieving 88% sentiment prediction accuracy. Built interactive dashboard using Streamlit and data visualisation libraries, providing real-time market insights for 100+ users. Deployed on Hugging Face Spaces with 95% uptime, demonstrating expertise in MLOps and model deployment.
Airbnb Data Cleaning & Exploratory Data Analysis
June 23, 2026 – Present
Built a systematic data cleaning pipeline on a real-world Airbnb dataset using Python (Pandas, NumPy), handling missing values, duplicate removal, data-type fixing, and feature standardisation. Performed outlier detection and treatment using boxplots, removing unrealistic price entries to ensure analytical reliability. Conducted EDA uncovering key insights: price distributions are heavily right-skewed, entire homes command significantly higher rates, and certain neighbourhoods dominate listings. Visualised patterns across price, room type, and location using Matplotlib and Seaborn; project hosted on GitHub.
Hospital Data Management System
June 23, 2026 – Present
Designed and implemented a comprehensive SQL database with 15+ interconnected tables. Optimised complex queries for patient demographics and treatment analysis, reducing report generation time by 50%. Developed automated reporting dashboards that improved data analysis efficiency by 30% for healthcare decisions. Created data warehouse architecture supporting real-time analytics for a 200-bed hospital operation.
AWS Academy Graduate - Machine Learning Foundations
Amazon Web Services
January 1, 2024 – Present
Python Programming Certification with Excellence
Scaler Academy
January 1, 2024 – Present
SQL Database Design and Optimisation
Scaler Academy
January 1, 2024 – Present
JPMorgan Chase Corporate Analyst Development Program (CADP)
Virtual Experience
January 1, 2024 – Present
TCS MasterCraft DataPlus - Data Analytics
Tata Consultancy Services
January 1, 2024 – Present
HP Data Science & Analytics Certification
Hewlett Packard
January 1, 2024 – Present
Machine Learning I
Columbia University / edX
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from financial market analysis to hospital data management and Airbnb data cleaning, shows a broad interest and adaptability to different domains. Their involvement in an AI Developer internship and multiple certifications in Machine Learning and Data Science align well with the target AI/ML Engineer role. The breadth of technical skills and exposure to various tools and platforms suggest a proactive learning attitude, which is a positive indicator for cultural fit in a dynamic technical environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills, cross-functional collaboration, and effective communication, as evidenced by their project descriptions and internship experiences. Their ability to translate complex technical concepts into business-friendly content (QSecuring Technologies) suggests good communication for stakeholder engagement. The experience in optimizing model performance and creating data quality frameworks indicates an operational mindset focused on efficiency and reliability.