May 2024 — Nov 2024
full stack developer intern
Worked on complex production-grade systems, building full-stack applications with TypeScript, React/Next.js, Vite, shadcn UI, Node.js, and Express, integrated with PostgreSQL and Edge Functions. Developed and deployed AI/ML projects using Python (NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch), transforming models into production-ready APIs and UI workflows.
Year :
2024
Industry :
IT Services Based Company
Company :
NoviTech Pvt Ltd
Internship Duration :
9 weeks
Problem :
The company faced challenges in integrating complex AI/ML workflows into production-grade full-stack systems. Distributed microservices, containerized pipelines, and real-time data processing lacked cohesive integration, while backend performance bottlenecks and inefficient algorithms slowed down feature delivery. Additionally, AI/ML models were underutilized, as they were not seamlessly embedded into user-facing applications or operational pipelines, limiting their business impact.
Solution :
To address these challenges, I developed full-stack solutions integrating AI/ML models directly into production workflows. I built responsive frontends using React/Next.js, Vite, and shadcn UI, and implemented optimized backend services with Node.js, Express, and PostgreSQL, including Edge Functions with RLS policies. I designed and deployed end-to-end ML pipelines in Python (NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch) covering data ingestion, preprocessing, model training, and inference, and exposed them via APIs for seamless integration into UI workflows. I also optimized backend performance using C++ and advanced DSA for algorithmic efficiency. All components were deployed in distributed, containerized environments with CI/CD pipelines, ensuring robust, scalable, and production-ready full-stack systems.
Challenge :
Integrating AI/ML workflows into production-grade full-stack systems was challenging due to distributed microservices, backend performance bottlenecks, and the lack of seamless model-to-UI integration.
Summary :
Developed production-ready full-stack systems integrating AI/ML workflows, optimized backend algorithms, and distributed microservices, delivering scalable, high-performance applications with seamless model-to-UI integration.
May 2024 — Nov 2024
full stack developer intern
Worked on complex production-grade systems, building full-stack applications with TypeScript, React/Next.js, Vite, shadcn UI, Node.js, and Express, integrated with PostgreSQL and Edge Functions. Developed and deployed AI/ML projects using Python (NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch), transforming models into production-ready APIs and UI workflows.
Year :
2024
Industry :
IT Services Based Company
Company :
NoviTech Pvt Ltd
Internship Duration :
9 weeks
Problem :
The company faced challenges in integrating complex AI/ML workflows into production-grade full-stack systems. Distributed microservices, containerized pipelines, and real-time data processing lacked cohesive integration, while backend performance bottlenecks and inefficient algorithms slowed down feature delivery. Additionally, AI/ML models were underutilized, as they were not seamlessly embedded into user-facing applications or operational pipelines, limiting their business impact.
Solution :
To address these challenges, I developed full-stack solutions integrating AI/ML models directly into production workflows. I built responsive frontends using React/Next.js, Vite, and shadcn UI, and implemented optimized backend services with Node.js, Express, and PostgreSQL, including Edge Functions with RLS policies. I designed and deployed end-to-end ML pipelines in Python (NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch) covering data ingestion, preprocessing, model training, and inference, and exposed them via APIs for seamless integration into UI workflows. I also optimized backend performance using C++ and advanced DSA for algorithmic efficiency. All components were deployed in distributed, containerized environments with CI/CD pipelines, ensuring robust, scalable, and production-ready full-stack systems.
Challenge :
Integrating AI/ML workflows into production-grade full-stack systems was challenging due to distributed microservices, backend performance bottlenecks, and the lack of seamless model-to-UI integration.
Summary :
Developed production-ready full-stack systems integrating AI/ML workflows, optimized backend algorithms, and distributed microservices, delivering scalable, high-performance applications with seamless model-to-UI integration.
May 2024 — Nov 2024
full stack developer intern
Worked on complex production-grade systems, building full-stack applications with TypeScript, React/Next.js, Vite, shadcn UI, Node.js, and Express, integrated with PostgreSQL and Edge Functions. Developed and deployed AI/ML projects using Python (NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch), transforming models into production-ready APIs and UI workflows.
Year :
2024
Industry :
IT Services Based Company
Company :
NoviTech Pvt Ltd
Internship Duration :
9 weeks
Problem :
The company faced challenges in integrating complex AI/ML workflows into production-grade full-stack systems. Distributed microservices, containerized pipelines, and real-time data processing lacked cohesive integration, while backend performance bottlenecks and inefficient algorithms slowed down feature delivery. Additionally, AI/ML models were underutilized, as they were not seamlessly embedded into user-facing applications or operational pipelines, limiting their business impact.
Solution :
To address these challenges, I developed full-stack solutions integrating AI/ML models directly into production workflows. I built responsive frontends using React/Next.js, Vite, and shadcn UI, and implemented optimized backend services with Node.js, Express, and PostgreSQL, including Edge Functions with RLS policies. I designed and deployed end-to-end ML pipelines in Python (NumPy, Pandas, Scikit-Learn, TensorFlow, PyTorch) covering data ingestion, preprocessing, model training, and inference, and exposed them via APIs for seamless integration into UI workflows. I also optimized backend performance using C++ and advanced DSA for algorithmic efficiency. All components were deployed in distributed, containerized environments with CI/CD pipelines, ensuring robust, scalable, and production-ready full-stack systems.
Challenge :
Integrating AI/ML workflows into production-grade full-stack systems was challenging due to distributed microservices, backend performance bottlenecks, and the lack of seamless model-to-UI integration.
Summary :
Developed production-ready full-stack systems integrating AI/ML workflows, optimized backend algorithms, and distributed microservices, delivering scalable, high-performance applications with seamless model-to-UI integration.