Sanskar Naithani

Web Developer

Talent

DehradunMember since July 9, 2026

HTML5CSS3BootstrapJavaScriptCross-Browser CompatibilityPHPNode.jsJavaSpring BootMySQLSQLiteGitAgilePythonSQLTensorFlowKerasOpenCVCNNDeep Face

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HTML5CSS3BootstrapJavaScriptCross-Browser Compatibility

About

Web Developer with hands-on experience building responsive and user-friendly web applications using HTML5, CSS3, Bootstrap, JavaScript, PHP, Java, and MySQL. Skilled in landing page development, responsive design, cross-browser compatibility testing, website optimization, form integration, and web deployment. Experienced working in Agile environments through internships and academic projects. Seeking opportunities in web development and full-stack application development.

Experience

Artificial Intelligence Intern - Wayeva Innovations - Remote

(2026-01 - 2026-04)

Designed and shipped Python-based AI/ML inference pipelines for 2 product features across 4 Agile sprint cycles, collaborating via Git with a cross-functional remote team.
Reduced end-to-end prediction latency by 20% through batch processing, code refactoring, and inference path optimization, directly improving application responsiveness.
Enforced branch hygiene and structured code review practices across a distributed team, achieving zero merge conflicts across all sprint deliverables.
Authored AI feature specifications and test-result documentation; templates adopted as the team standard for iterative ML feature delivery.

Artificial Intelligence Intern - Codec Technologies Pvt. Ltd. - Remote

(2025-10 - 2025-11)

Engineered end-to-end computer vision pipelines from raw image ingestion to model serving using Python, TensorFlow, and CNN architectures; achieved 92% test accuracy on classification tasks.
Built a reusable preprocessing and augmentation library across 1,000+ training samples, standardizing input quality and cutting per-experiment data-preparation time.
Applied transfer learning on pretrained CNN models, reducing training convergence time by 30% while maintaining classification performance on domain-specific datasets.