ARCHANA (System Engineer-AI)

Dynamic System Engineer with a proven track record at Infosys BPM Limited, specializing in AI system design and deployment. Expert in machine learning and data analytics, I excel in optimizing model performance while demonstrating strong communication skills. Committed to driving innovation and enhancing operational efficiency in AI strategies.

• Microsoft Azure AI Fundamentals (AI-900), Foundational AI concepts & Azure AI services
• Microsoft Azure Fundamentals (AZ-900), Core Azure cloud services
• Microsoft Azure Data Fundamentals (DP-900), Data concepts & Azure data services
• Microsoft Azure Data Scientist Associate (DP-100), ML solutions on Azure
• Microsoft Power Platform Fundamentals (PL-900), Power Platform capabilities
• Microsoft Azure AI Engineer Associate (AI-102), Building & deploying AI solutions
• Infosys Certified Python Programmer, Proficiency in Python
• Infosys Certified Responsible AI Practitioner, Ethical & responsible AI practices

• Project I– SmartEmailAssistant • Built an Azure Function App integrated with Fast API to automate multilingual email processing for cost efficiency. • Used Azure Language Service for Translation. • Used Azure OpenAI Services to extract relevant information.
• Project II – Intel Interaction Analytics • Built an Azure Function App with Fast API Endpoints to automate sentiment analysis, PII masking, agent evaluation and topic modelling. Orchestrated NLP pipelines to standardize evaluations and ensure compliance, improving processing speed and consistency across workflows.
• Project III – Offensive Language Prediction • Implemented ChatGPT API for content moderation for reducing offensive content. • Built real-time comment analysis tools and a moderation dashboard to improve efficiency. • Leveraged Azure OpenAI and NLP techniques for accurate detection and automated filtering.
• Project IV – Vendor Query Email Classifier .Built and deployed a Proof of Concept (POC) machine learning model for Vendor Query Email Classification for Evident client, addressing class imbalance and enhancing email categorization efficiency.