I am an AI/ML Engineer with 5+ years of experience delivering scalable enterprise AI and machine learning solutions across consulting and technology environments. I design and deploy end-to-end ML pipelines using PyTorch, Scikit-learn, and Apache Spark, improving model performance on fraud detection, churn, and demand forecasting with datasets of 20M+ records. I accelerate deployment by building robust MLOps with MLflow, Docker, and CI/CD, enabling real-time inference that handles thousands of predictions per minute. I build streaming and batch architectures with Kafka and Spark Streaming to generate near real-time insights from millions of daily events, and I translate complex business challenges into production-grade AI solutions deployed across AWS and Azure, delivering measurable impact in risk analytics, customer intelligence, and forecasting.

Phanendhar Reddy

I am an AI/ML Engineer with 5+ years of experience delivering scalable enterprise AI and machine learning solutions across consulting and technology environments. I design and deploy end-to-end ML pipelines using PyTorch, Scikit-learn, and Apache Spark, improving model performance on fraud detection, churn, and demand forecasting with datasets of 20M+ records. I accelerate deployment by building robust MLOps with MLflow, Docker, and CI/CD, enabling real-time inference that handles thousands of predictions per minute. I build streaming and batch architectures with Kafka and Spark Streaming to generate near real-time insights from millions of daily events, and I translate complex business challenges into production-grade AI solutions deployed across AWS and Azure, delivering measurable impact in risk analytics, customer intelligence, and forecasting.

Available to hire

I am an AI/ML Engineer with 5+ years of experience delivering scalable enterprise AI and machine learning solutions across consulting and technology environments. I design and deploy end-to-end ML pipelines using PyTorch, Scikit-learn, and Apache Spark, improving model performance on fraud detection, churn, and demand forecasting with datasets of 20M+ records.

I accelerate deployment by building robust MLOps with MLflow, Docker, and CI/CD, enabling real-time inference that handles thousands of predictions per minute. I build streaming and batch architectures with Kafka and Spark Streaming to generate near real-time insights from millions of daily events, and I translate complex business challenges into production-grade AI solutions deployed across AWS and Azure, delivering measurable impact in risk analytics, customer intelligence, and forecasting.

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Experience Level

Expert
Expert
Expert
Expert
Expert
Expert
Expert
Expert

Work Experience

AI/ML Engineer at Deloitte
November 1, 2024 - Present
Design and deploy machine learning pipelines in Python (PyTorch, Scikit-learn, Pandas) to support enterprise use cases including fraud detection, customer churn prediction, and demand forecasting across datasets with 20M+ records. Build scalable data processing workflows using Apache Spark and SQL to transform and prepare 1–2 TB of financial and behavioral data, reducing data preparation time by 30% for downstream model training. Develop and tune models including XGBoost, Random Forest, and neural networks, improving model performance metrics (AUC/accuracy) by 15–20% across classification and regression problems. Deploy production models as REST APIs using FastAPI and Docker, supporting real-time inference services handling 5K+ prediction requests per minute across client applications. Implement model lifecycle management using MLflow and CI/CD pipelines, reducing model deployment timelines from 10 days to under 5 days and improving experiment traceability. Build streaming and batc
Machine Learning Engineer at IBM
April 1, 2020 - June 1, 2023
Designed and deployed end-to-end machine learning solutions for enterprise clients across banking and retail sectors, impacting $200M+ in business operations through predictive analytics and optimization models. Built supervised and unsupervised models (XGBoost, Random Forest, Neural Networks) on datasets exceeding 10M+ records, improving prediction accuracy by 20–25% across use cases. Developed scalable data pipelines using Python and SQL for feature engineering and preprocessing, reducing data preparation time by 35% and improving model reliability. Implemented NLP-based solutions for document classification and sentiment analysis, increasing processing efficiency by 40% compared to manual review workflows. Deployed ML models into production using Docker and Kubernetes, reducing model release cycles by 30% and ensuring high-availability inference endpoints. Built automated monitoring and drift detection systems, reducing model performance degradation incidents by 38% in live enviro

Education

Masters in Information System Technologies at Wilmington University
January 11, 2030 - May 1, 2025

Qualifications

Add your qualifications or awards here.

Industry Experience

Financial Services, Retail, Professional Services