Anna is a MLOPs Engineer with 4. 5 years of experience in delivering transformative AI solutions across multiple sectors.
Successfully delivered production-level code by translating data scientist needs.
Implemented automated CI/CD pipelines for ML model deployment and monitoring across multiple projects.
Spearheaded the automation of machine learning workflows for scalability and reliability.
Increased coder efficiency by 30% by streamlining debugging and automating code generation.
Reduced diagnostic review times by 25% and enhanced prediction accuracy in Radiology AI for bone fracture detection.
Achieved 95% accuracy in identifying at-risk customers and reduced churn by 20%.
Overview: Developed a web application leveraging GPT-4 in Azure OpenAI for code correction and generation. Responsibilities: Designed and implemented the web application on Azure. Integrated GPT-4 via Azure OpenAI for core AI functionalities. Utilized RAG for data customization and Assistant API for structured outputs. Deployed the solution via Azure Web App.
Key outcomes:
Increased coder efficiency by 30%.
Overview: Developed a computer vision-based API to predict bone fractures using YOLOv8, Faster R-CNN, and U-NET. Responsibilities: Developed a computer vision-based API leveraging YOLOv8, Faster R-CNN for classification and detection. Implemented MLOps processes including automated CI/CD pipeline with Azure DevOps and Azure ML.
Key outcomes:
Reduced diagnostic review times by 25%.
Overview: Developed a churn prediction system using Python, Azure ML, and Power BI. Responsibilities: Developed the churn prediction system using Python and deployed it on Azure ML. Utilized Power BI to analyze customer data and visualize insights.
Key outcomes:
Achieved 95% accuracy in identifying at-risk customers.
Recommendation System for Prospect Identification — ML-based system for identifying similar + high-potential prospects. 40% success-to-approach lift.
Key outcomes:
Improved the success-to-approach ratio by 40%, enabling more targeted marketing.
Increased the overall efficiency of customer acquisition and engagement strategies.
Supply Chain Optimization — ML model for inventory cost reduction + delivery efficiency in manufacturing + pharma.
Key outcomes:
Reduced inventory costs and improved delivery efficiency.
Anna
MLops Developer