I’m an AI/ML Engineer with 4+ years of experience designing, deploying, and optimizing machine learning and NLP solutions across finance and healthcare. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face, and I have a track record of building scalable data pipelines, feature engineering, and production deployments using AWS, Azure, Docker, and Kubernetes. I turn complex data into actionable insights that improve decision-making and drive business value. I thrive in cross-functional, collaborative teams—partnering with product managers, data engineers, and risk/compliance to deliver measurable improvements from fraud detection to patient risk prediction. I’m passionate about model optimization, integration of LLMs, and responsible AI practices, with a focus on delivering practical, impact-driven solutions.

Upendar Reddy Kandimalla

I’m an AI/ML Engineer with 4+ years of experience designing, deploying, and optimizing machine learning and NLP solutions across finance and healthcare. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face, and I have a track record of building scalable data pipelines, feature engineering, and production deployments using AWS, Azure, Docker, and Kubernetes. I turn complex data into actionable insights that improve decision-making and drive business value. I thrive in cross-functional, collaborative teams—partnering with product managers, data engineers, and risk/compliance to deliver measurable improvements from fraud detection to patient risk prediction. I’m passionate about model optimization, integration of LLMs, and responsible AI practices, with a focus on delivering practical, impact-driven solutions.

Available to hire

I’m an AI/ML Engineer with 4+ years of experience designing, deploying, and optimizing machine learning and NLP solutions across finance and healthcare. I’m proficient in Python, TensorFlow, PyTorch, and Hugging Face, and I have a track record of building scalable data pipelines, feature engineering, and production deployments using AWS, Azure, Docker, and Kubernetes. I turn complex data into actionable insights that improve decision-making and drive business value.

I thrive in cross-functional, collaborative teams—partnering with product managers, data engineers, and risk/compliance to deliver measurable improvements from fraud detection to patient risk prediction. I’m passionate about model optimization, integration of LLMs, and responsible AI practices, with a focus on delivering practical, impact-driven solutions.

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Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
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Language

English
Fluent

Work Experience

AI/ML Engineer at Brex
October 1, 2024 - Present
Led an AI-powered Expense Intelligence System; collaborated with product managers, finance teams, and data engineers to deliver personalized spend insights, fraud detection, and budget optimization tools for corporate clients. Built data pipelines on AWS S3; performed data cleaning and feature extraction to produce high-quality datasets for model development. Implemented reinforcement learning models in TensorFlow and OpenAI Gym to optimize real-time budget recommendations and anomaly alerts. Engineered feature pipelines (user segmentation, temporal spend patterns, merchant behavior encoding); utilized AWS SageMaker for scalable training and hyperparameter tuning. Integrated Claude 2 for natural-language summaries of spend insights and anomaly explanations. Applied NLP (NER, text classification) to extract merchant attributes and improve downstream feature quality. Applied Bayesian optimization for parameter tuning and thresholds; conducted extensive A/B tests to boost smart spend insi
Data Scientist at Omega Healthcare
January 1, 2021 - August 1, 2023
Developed a patient risk prediction system using ensemble models and Temporal Graph Convolutional Networks to model patient-provider interactions, improving early identification of high-risk patients and reducing readmission rates. Built end-to-end data pipelines on Azure Databricks processing 75M+ daily patient transactions with 99.4% pipeline reliability. Designed T-GCNs with PyTorch Geometric and DGL to predict disease progression, achieving 31% higher AUC than baselines. Implemented rolling-window health metrics and treatment adherence trends to reduce inference time by 33% while preserving accuracy. Deployed models on Azure Kubernetes Service with CI/CD, monitored drift with Grafana, and translated insights into actionable clinical interventions that increased patient engagement by 18%.

Education

Master of Science in Computer Science at Auburn University at Montgomery
August 1, 2023 - May 1, 2025
Bachelor of Technology in Electrical and Electronics Engineering at Sri Indu College of Engineering & Technology
August 1, 2018 - May 1, 2022

Qualifications

AWS Certified Developer – Associate
January 11, 2030 - June 29, 2026
Getting Started with AWS Machine Learning
January 11, 2030 - June 29, 2026
Introduction to Cloud Identity
January 11, 2030 - June 29, 2026
Data Visualization using Plotly
January 11, 2030 - June 29, 2026
Code Yourself! An Introduction to Programming
January 11, 2030 - June 29, 2026

Industry Experience

Financial Services, Healthcare, Software & Internet, Professional Services, Media & Entertainment

Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate
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