Professional Profile - All About Me
I am a Software Engineer with 4 years of experience specializing in AI/ML, Cloud Computing (Azure, AWS), and innovative software solutions. With a Master's in Computer Science from the University of Cincinnati, I've developed expertise in creating scalable AI applications, implementing RAG systems, and optimizing cloud infrastructure.
Professionally, I've contributed to AI teams at companies like Siemens Digital Industries Software, where I designed and built scalable AI solutions leveraging Generative AI and cloud technologies. I have expertise in machine learning pipelines, deep learning models, and natural language processing techniques. My technical skills include Python, C++, Java, SQL, Postgres, and frameworks like LangChain, LlamaIndex, and various cloud platforms including AWS (Amazon Bedrock, SageMaker) and Azure (Cosmos DB, AI Search).
As an AI Engineer with strong software development foundations, I excel at bridging the gap between cutting-edge AI research and practical business applications. I'm passionate about solving complex problems through AI innovation, and I welcome opportunities to collaborate on projects involving machine learning, cloud architecture, and software development.
• Designed and built scalable AI solutions leveraging Generative AI, Azure, Python, SQL, Cosmos DB, and Mendix.
• Automated domain model customization with RAG, achieving 75% automation and reducing manual effort.
• Revamped AI Use Case Tracker with Postgres, vector DBs, and similarity search; built text-to-speech for Xcelerator Academy, reducing video creation effort by 40%.
• Served as Azure SME and admin, reducing cloud costs by 40% ($4,000/month) via strategic monitoring and optimization.
• Led Advantedge AI program, collaborating globally to refine use cases and automate workflows with Power Automate and Git.
• Engineered end-to-end ML pipelines (Sk-learn, Keras) for high-dimensional clinical imaging, boosting diagnostic accuracy by 15%.
• Integrated NLP (BERT/GPT) with Apache Spark to automate clinical text processing, reducing prep time by 30%.
• Deployed CNN and transformer architectures for research workflows and real-time insights.
• Developed and maintained AI/ML features for Artis Angiography & X-Ray Systems using C++, C#, SQL, AWS, and Linux.
• Built medical imaging AI for motion artifact correction, reducing error rates by 35%.
• Established real-time anomaly detection with federated learning, reducing equipment downtime by 25%.
• Led Backup & Restore integration, optimized remote updates, and increased CI/CD test automation from 30% to 80%.
• Recognized as Top Performer for critical contributions and global team collaboration.
• Engineered custom ML models (Python, TensorFlow, Keras) to simulate automotive components, improving design accuracy by 20%.
• Implemented computer vision for automated QA, integrating IoT sensor data for predictive maintenance.
• Orchestrated cloud-based AI frameworks on AWS/Azure for real-time analytics, reducing downtime by 30%.
• Enhanced testing pipelines and mentored colleagues in ML methodologies.
Related Courses: Machine Learning, Deep Learning, Artificial Intelligent, Computer Vision, NLP, Cloud Computing, Algorithms, Database Management Systems, Data Analytics
Related Courses: Programming, Software Engineering, OOPs, Data Structures and Algorithms, Computational Mathematics
See My Work - Projects
This Personal Assistant Chatbot project leverages Streamlit, OpenAI's GPT-4, AWS and various APIs to provide an intelligent conversational assistant. Key features include document processing, web search, weather updates, and the implementation of Retrieval-Augmented Generation (RAG) for enhanced response accuracy and relevance.
View Project
Face Mask Detection is a computer vision project utilizing deep learning to automatically identify whether individuals are wearing face masks in images or videos. Built primarily with Jupyter Notebook and Python, it features data preprocessing, model training, and real-time detection capabilities, making it ideal for health and safety monitoring.
View Project
This project uses machine learning to predict liver disease from clinical data. Key features include data preprocessing, visualization, and multiple ML models for accurate predictions. Built with Python and ready for healthcare analytics and research applications.
View Project
Implementation of a Database System in C, featuring core components such as query parsing, indexing, transaction management, and data storage. This project demonstrates foundational database principles and efficient data operations for educational and practical purposes.
View Project
Cincinnati, OH, USA
+1 (513)-250-9969
perlasaicharanreddy@gmail.com