Neha Bollu

Experienced Azure DevOps Engineer with 3+ years in automating deployments and managing cloud infrastructure.
Proficient in using Azure DevOps, Git, Docker, Kubernetes, and Terraform for modern DevOps workflows.
Hands-on expertise with Azure services like App Services, AKS, VMs, and Storage solutions.
Skilled in designing and maintaining CI/CD pipelines using YAML and integrating automated tests.
Well-versed in infrastructure as code with Terraform and ARM templates for scalable environments.
Strong knowledge of monitoring and alerting tools, including Azure Monitor and Log Analytics.
Effective team player in Agile settings, ensuring smooth collaboration across development and ops.
Committed to building secure, efficient, and resilient cloud-based systems.

SkillMentor: Elevating Interview Success and Beyond

Role: Frontend Developer

Project Description: Introducing our comprehensive interview preparation platform that bridges the gap between students and colleges. Students can enhance their skills through real interview experiences, receive detailed feedback, and track their progress. College admins have a powerful tool at their disposal, allowing them to monitor and mentor students effectively, ensuring their success in both soft and hard skills.

Responsibilities:

• Developed and maintained responsive, user-friendly interfaces using React.js, Vite.js, and Tailwind CSS to enhance the user experience for students and college administrators.
• Integrated real-time feedback and progress tracking features, enabling students to receive actionable insights and monitor their interview preparation journey.
• Collaborated with UX/UI designers and backend developers to ensure seamless integration of APIs, delivering a cohesive and interactive platform experience.
• Implemented dynamic dashboards and admin panels for college administrators to effectively monitor student performance and provide targeted mentorship.
• Optimized application performance and load times by leveraging Vite.js for fast builds and Tailwind CSS for efficient styling, ensuring a smooth and responsive user experience across devices.

CronSecure: Automating Linux Lab Management

Project Description:This project involved managing a lab environment with Linux (Ubuntu, CentOS) and Windows systems to ensure optimal performance and reliability. It included automating user account management and scheduled tasks using tools like crontab to streamline system administration. Access controls and user permissions were configured to enhance security and ensure compliance with IT policies.

Responsibilities:

• Acquired practical experience in administering Linux (Ubuntu, CentOS) and Windows operating systems within a lab setup, emphasizing system stability and optimal performance.
• Performed user account administration tasks such as creating, removing, and grouping user accounts to support efficient system management and organizational policies.
• Developed and implemented automated task scheduling using tools like crontab to enforce user login restrictions and initiate system shutdowns at defined times.
• Set up and maintained access control mechanisms and user permission settings to strengthen system security and prevent unauthorized access.
• Carried out user management operations including account creation, deletion, and group configuration to streamline administrative duties.
• Built scheduling routines for enforcing login policies and automating system shutdowns using crontab for operational consistency.
• Applied access management strategies to secure systems and ensure adherence to institutional IT governance standards.

Wine Quality Qualification

Project Description:Designed and developed a predictive machine learning model to evaluate wine quality based on chemical composition and sensory data. Leveraged advanced data science techniques to automate the assessment process and provide actionable insights for winemakers. Focused on end-to-end model development, from data collection and preprocessing to model training, evaluation, and deployment. The project enhanced decision-making for production optimization and improved consumer guidance for wine selection.

Responsibilities:

• Collected, cleaned, and preprocessed complex wine datasets, ensuring high data quality, integrity, and consistency for accurate model training.
• Engineered features and applied statistical analysis to identify key factors influencing wine quality, enhancing model interpretability.
• Developed and trained machine learning models (Random Forest, SVM, Logis6c Regression), performing hyperparameter tuning and cross-validation to optimize performance.
• Deployed scalable, reproducible models and maintained robust version control using Git, ensuring seamless future integration and collaboration.

• AWS Certified Cloud Practitioner
• AWS Certified Solutions Architect – Associate
• Microsoft Certified: Azure Fundamentals