Vamsi

Hyderabad, Telangana, India
Vamsi
Experience: 2 years
Open to: Full-Time
Education: Bachelors
Availability: Within 30 Days
Skills: Active Directory, Azure, Django, Docker, Matplotlib, NumPy, pandas, PostgreSQL, Python3, React, Redis, scikit-learn
Previously worked at: IT Service Companies
Assessment Score: 100
  • Experience and Expertise: Digital Specialist Engineer with 2+ years of experience in Python, Django, Machine Learning, and Azure, focused on deploying scalable solutions for cloud-based applications and forecasting models. Proven ability in enhancing backend APIs, implementing machine learning pipelines, and optimizing data workflows for high-impact clients.
  • Key Achievements: Successfully improved model accuracy by 5% and reduced inference time by 98% for a Microsoft client by tuning hyperparameters and deploying forecasting models using Azure Pipelines. Designed and implemented RESTful APIs with Django REST Framework, improving backend efficiency for Microsoft’s FoundersHub.
  • Technical Skills: Expertise in backend development with Python, Django, and NodeJs, and frontend technologies including ReactJs, NextJs, and TypeScript. Proficient in machine learning and deep learning frameworks such as TensorFlow, Pandas, Scikit-learn, and XGBoost, alongside cloud technologies like Azure, Docker, and PostgreSQL.
  • Project Experience: Developed a time series forecasting model to predict power consumption using XGBoost, achieving an RMSE of 2.0. Created a chat application with Retrieval-Augmented Generation (RAG) for querying PDFs, leveraging Langchain, GPT-4, and Chroma for data retrieval and interactive querying.
  • Educational Background: Holds a Bachelor of Technology in Electronics and Communication Engineering from MLR Institute of Technology, Hyderabad, with a strong foundation in technical problem-solving and software development.
  • Certifications and Additional Skills: Completed specialized courses in Mathematics for Machine Learning and Deep Learning from Coursera. Demonstrated leadership by conducting knowledge transfer sessions on machine learning concepts and model applications for team members.

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