Nikhitha Kurra
Entry-level Machine Learning Engineer • Khammam, Telangana, India • n**************@gmail.com • +91*******553 • linkedin.com/••••• • drivetube.ai/•••••
Professional Summary
Machine Learning Engineer with 0 years of experience in AI & ML, data analytics, and QA testing. Skilled in Python, SQL, computer vision (YOLO/OpenCV), scikit-learn, Pandas, and test-driven QA processes. Experienced in building end-to-end ML prototypes and supporting data-driven decision-making during internships; seeking an entry-level ML Engineer, Data Analyst, or QA Engineer role to deliver reliable technical solutions and measurable business value.
Technical Skills
Programming Languages: Python,C,JavaScript
Web Technologies: HTML,CSS,JSON
Frameworks and Libraries: Scikit-learn,OpenCV,Pandas,NumPy,Matplotlib,Seaborn
Databases: SQL,MySQL
Data and Analytics: XGBoost,Random Forest,Ensemble Learning,Predictive Modelling,LLMs,Prompt Engineering,Anthropic Claude API,Data Visualization,Microsoft Excel,Jupyter Notebook
Tools and Methodologies: Git,GitHub,Jira,Agile,VS Code
Computer Vision & NLP: Computer Vision,YOLO,NLP
Work Experience
The Voila Tech
QA Intern
May 2026 – Present
Worked at a software company delivering web-based applications; supported QA activities to ensure stable feature releases and production readiness.
Tech Stack: Jira, Git, GitHub, VS Code, Agile
- Performed manual and functional testing across web application features each sprint, creating and executing test cases to validate requirements and acceptance criteria using Jira.
- Reproduced, triaged, and documented defects in Jira; collaborated directly with developers to verify fixes and close issues during regression cycles.
- Authored and maintained test-case documentation and checklists to standardize QA coverage for new features and bug fixes, improving handover clarity.
- Executed cross-browser and responsive testing to identify UI and compatibility issues across major browsers and device sizes before releases.
- Participated in Agile ceremonies, provided QA status updates and risk assessments, and coordinated release verifications with the development team.
- Conducted exploratory testing and pre-release validation on builds to ensure deployment readiness and reduce post-release incidents.
Bluestock Fintech
Data Analyst Intern
Apr 2026 – May 2026
Interned at a fintech firm supporting operational and business analytics; work focused on cleaning, analyzing, and visualizing business data to support decisions.
Tech Stack: Python, Pandas, NumPy, MySQL, Microsoft Excel, Git, Jira
- Collected, cleaned, and validated operational datasets using Python and Pandas to prepare transaction and business data for analysis.
- Wrote SQL queries against MySQL databases to extract cohorts and compute key metrics used by operations and product teams.
- Performed exploratory data analysis and feature engineering to surface trends and factors affecting business KPIs.
- Built reports and visualizations using Python and Microsoft Excel to communicate findings and recommended actions to stakeholders.
- Documented analysis methodology, maintained basic data dictionaries, and delivered concise stakeholder reports under mentor guidance.
- Automated recurring report generation using Pandas and Excel templates to streamline weekly operational decision-making.
Projects
AI-Based CCTV Surveillance for Crowd Management & Crime Prevention | Jan 2026 – May 2026
Tools Used: Python, YOLO, OpenCV, Machine Learning, Computer Vision
- Designed an end-to-end computer vision pipeline to process CCTV video streams for crowd density estimation and suspicious-activity detection.
- Implemented YOLO-based object detection and OpenCV tracking to detect and follow individuals and compute density metrics in near real time.
- Optimized the inference pipeline for low-latency processing and demonstrated a proof-of-concept that leveraged existing CCTV hardware without additional infrastructure.
ML Model for Predicting and Tracking Student Performance | Aug 2025 – Dec 2025
Tools Used: Python, SQL, Pandas, Scikit-learn, XGBoost
- Aggregated attendance, marks, and participation datasets and engineered predictive features to flag at-risk students.
- Trained and tuned ensemble models including Random Forest, XGBoost, and Logistic Regression using cross-validation to optimize F1 and ROC-AUC.
- Evaluated model performance, produced visualizations and reports, and recommended targeted interventions to support data-driven educational decisions.
Education
Priyadarshini Institute of Science and Technology for Women
B.Tech, Artificial Intelligence & Machine Learning • 2022 – 2026
Sri Chaitanya Junior College
Intermediate (Class XII) • 2020 – 2022
Pragathi Model School
SSC (Class X) • 2019 – 2020
Certifications
Anthropic Claude AI Fundamentals Certification — Anthropic • 2026
Data Analyst Internship Certificate — Bluestock Fintech • 2026
Deloitte Data Analytics Job Simulation — Forage • 2025
Cybersecurity Analyst Job Simulation — Forage • 2025
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