• Experienced Senior Software Engineer with over 6+ years in the IT and Telecom industries, focusing on AI-based integration solutions.
• Expertise in Large Language Models (LLMs), Copilot integration, data governance, and automation of business workflows.
• Proven ability to design and implement AI-powered solutions for real-time ticketing, knowledge management, and chatbot-driven support systems.
• Extensive experience in cloud technologies, enterprise software architecture, and data integration.

Programming Languages: Python, Java, SQL

AI Tools: GPT-3/4, OpenAI, MS Copilot, Azure Cognitive Services

Cloud Platforms: Azure, AWS, Databricks

Data Integration: APIs, SQL, PostgreSQL, SharePoint, CRM

ETL & Data Processing: PySpark, Airflow, AWS Glue

Automation: AI-driven automation in CRM, ticketing systems, and offer generation

DevOps: Docker, Kubernetes, CI/CD

Real-Time Ticketing System with AI-based Internal Notes

Tech Stack: GPT-4, Azure, Python, PostgreSQL, APIs

Description: Developed an AI-powered solution for a real-time ticketing system that automatically generates internal notes in response to incoming tickets.

Key Contributions:

• Integrated an LLM with a knowledge base and previous ticket data to generate draft responses for agents, reducing response times by 40%.
• Ensured seamless integration of GPT-4 with internal ticketing systems, improving ticket resolution quality.

iWMS ERP Software

Tech Stack: MS Copilot, Python, Azure Data Lake, APIs, SharePoint, CRM

Description: Led the integration of MS Copilot to streamline AI-driven responses across various internal data sources, including CRM, SharePoint, and a ticketing system.

Key Contributions:

• Designed an AI chatbot that pulled real-time information from multiple systems to deliver accurate and context-aware answers to internal queries, significantly improving operational efficiency.
• Built a knowledge base integration that connected licenses, contracts, and documentation, enabling quick retrieval via natural language searches.

Customer Churn Prediction Pipeline

Tech Stack: Azure Data Factory, Databricks, Data Lake, Python, APIs

Description: Designed and deployed a pipeline to process customer data from Salesforce and JIRA into a central data lake.

Key Contributions:

• Integrated AI models with ticketing systems to provide insights into customer behavior and predicted churn, enhancing customer retention strategies.
• Implemented AI-driven support to extract relevant data from documentation and provide internal note suggestions.
  

Automated Offer Generation System

Tech Stack: Python, GPT-4, APIs, CRM, PostgreSQL

Description: Developed an AI-based system for generating offers using predefined templates, automating the insertion of pricing tables and calculations.

Key Contributions:

• Ensured precise formatting and error-free calculations through a Python-based AI script, reducing manual effort by 70%.
• Integrated CRM and existing templates with the AI engine to create dynamic and personalized offers for customers.
  

Real-Time Fuel Consumption Optimization Pipeline

Tech Stack: Azure IoT Hub, Python, Redshift

Description: Developed an AI-enhanced data model to ingest and analyze IoT sensor data, optimizing fuel consumption.

Key Contributions:

• Automated real-time data processing, improving the operational efficiency of IoT systems.
• Implemented CDC (Change Data Capture) and SCD (Slowly Changing Dimensions) Type 1 and 2 to maintain historical accuracy and data integrity.
  

Data Governance and API Integration for Web-Based EHR Systems

Tech Stack: AWS Glue, PySpark, APIs

Description: Led the transition of data from OLTP systems to advanced ETL workflows in AWS Glue, ensuring data consistency and integrity.

Key Contributions:

• Automated data retrieval and processing for integration with GPT models, enhancing AI-driven search capabilities within EHR systems.