Aakash Singh Azad Email & Phone Number

Hyderabad, Telangana, India

Hyderabad, Telangana, India

BrAInspark: | Python, Google Cloud Platform (GCP), Cloud Function, DataFlow, BigQuery, Gemini-1.5-pro-001• Developed and deployed a transformative project for Verizon to automate BigQuery query generation from English text.• Managed data flow from Google Cloud Storage staging to Dataflow processing, ensuring efficient data handling and storage in BigQuery.• Achieved 90% query accuracy using Gemini-1.5-pro-001, enhancing data retrieval speed and operational efficiency.• Implemented scalable cloud solutions, optimizing system performance and reducing operational costs by 30% through efficient resource utilization.NetworkEngineerAI: | Python, Flask, Langchain, FAISS, Huggingface, Colab• Developed a network engineer-centric chatbot for Verizon, leveraging the RAG (Retrieval-Augmented Generation) capabilities using the Langchain framework, achieving an 85% accuracy rate in query response.• Used PDF and ticket data(CSV) embedding with Huggingface’s model (Mixtral-8x7B-Instruct-v0.1) to perform a similarity search, resulting in a 40% reduction in resolution time for network engineering issues.• Demonstrated expertise in LLM (Large Language Model), showcasing AI solutions for domain-specific challenges, leading to a 30% improvement in problem-solving efficiency for network engineers in Verizon.Churn Analysis: | Python, Pandas, Matplotlib, Seaborn, Excel sheet, SQL• Identified and analyzed 400+ telecom-specific KPIs to tackle customer churn, employing Python (pandas) for robust data cleaning and maintenance.• Employed correlation mapping and visualizations (e.g., Correlation heat maps, Line Plots, Scatter Plots) to extract actionable insights.• Enabled data-driven strategies for reducing churn rates and enhancing operational excellence in the telecom industry.

Delhi, India

TwinVision: | Python, Flask, React.js, GitHub, Linear Regression Model, BigQuery, Docker, Pandas, Scikit-learn• Developed a Python Flask backend with integrated linear regression ML algorithm, achieving a 20% improvement in real-time parameter prediction accuracy.• Conducted comprehensive data preprocessing to optimize model performance, leveraging historical data from bigquery for predictive analytics.• Dockerized the application, reducing deployment time by 40% and ensuring scalability on the cloud (Cloud Run) for seamless deployment.

Bachelor Of Technology - Btech, Electrical And Electronics Engineering

Diploma In Engineering, Electrical Engineering