Machine Learning Engineer - OPT on-site, CA, AWS/Azure expert
Resume:
Ashish Dondapati
Machine learning Engineer
******.*********@*****.***
Machine Learning Engineer with 5+ years of experience building and deploying scalable ML solutions in cloud environments (AWS, Azure). Proven expertise in deep learning, Computer Vision, NLP, OCR, GANs, and MLOps practice with hands-on experience in automation and data infrastructure. Adept at improving system performance, reducing costs, and driving business outcomes with data-driven solutions.
Core Skills
• Programming & ML Frameworks: Python, TensorFlow, PyTorch, Scikit-learn, OpenCV, MLflow, GANs, Transformers, Azure OpenAI, AI Agents and Object Detection.
• Cloud & MLOps: AWS (Textract, SageMaker), Azure, Docker, Google Vertex AI
• Data Engineering & Analytics: SQL, Power BI, Data Pipelines, Feature Engineering, Predictive Analytics
• Other Tools: Git/GitHub, Jira, Django, Flask, Automation (RestAPI, APIs, Web Scraping), Unit Testing, HuggingFace, LangChain, RAGs, Docker, Kubernetes, Weaviate
• Agentic AI: Crew AI, OpenAI SDK, Autogen, LangGraph, LangChain, Semantic Kernel Work Experience
Machine Learning Engineer Intern
FedEx Jan 2025 - May 2025
• Developed an invoice data extraction pipeline using OCR engines (Tesseract & ABBYY) to process PDF documents with high accuracy (92%).
• Applied computer vision techniques for document layout analysis, enabling detection of key fields (invoice number, vendor, date, totals).
• Built preprocessing workflows (noise removal, skew correction, table detection) to improve OCR accuracy by 25%+.
• Automated structured data extraction and transformation into standardized formats
(CSV/JSON), streamlining downstream analytics.
• Integrated the pipeline with SQL storage and APIs, reducing manual invoice processing time by 60%.
• Evaluated and optimized OCR models with precision/recall metrics, achieving >90% field- level accuracy on test invoices.
Machine Learning Engineer Nov 2022 - Aug 2023
HP enterprise Bangalore
• Built data and reporting infrastructure using Power BI, Azure, and SQL, improving reporting accuracy and enabling 26% faster decision-making.
• Automated GitHub project release process using API & Auto Release, reducing release cycle time by 40%.
• Developed and deployed Django-based web apps for web scraping, boosting performance by 17%.
• Partnered with ML teams to deploy scalable models using MLflow for experiment tracking and model lifecycle management.
• Applied Bayesian optimization, reinforcement learning, active learning, transformers, diffusion models, and graph neural networks
Machine Learning Engineer June 2018 - Oct 2022
Mphasis Bangalore
• Designed & deployed classification, clustering, and deep learning models for predictive analytics.
• Implemented OCR pipelines using Tesseract & ABBYY, reducing document processing from day to hours.
• Developed tariff code recommendation system, reducing shipment processing time by 30% (5 FTE savings).
• Built and containerized GAN-based satellite image super-resolution, improving detection accuracy by 25%.
• Automated unit testing with Python, accelerating bug detection and improving code quality. Education
Colorado State University Aug 2023 - May 2025
Masters Computer Information Systems
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