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Hi, I'm Pavan Kumar, an AI/ML Engineer with over 4 years of experience in developing scalable machine learning models and intelligent recommendation systems. I specialize in the full machine learning lifecycle, including data preprocessing, model training, deployment, and monitoring, with a strong background in Python, PyTorch, and applied statistics. I have worked extensively on projects involving credit risk assessment, fraud detection, personalized user engagement, and real-time ML inference pipelines. I am passionate about leveraging AI/ML to deliver business value and enjoy collaborating with cross-functional teams to align AI solutions with organizational goals.…Hi, I'm Pavan Kumar, an AI/ML Engineer with over 4 years of experience in developing scalable machine learning models and intelligent recommendation systems. I specialize in the full machine learning lifecycle, including data preprocessing, model training, deployment, and monitoring, with a strong background in Python, PyTorch, and applied statistics. I have worked extensively on projects involving credit risk assessment, fraud detection, personalized user engagement, and real-time ML inference pipelines. I am passionate about leveraging AI/ML to deliver business value and enjoy collaborating with cross-functional teams to align AI solutions with organizational goals.

Pavan Kumar

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Hi, I'm Pavan Kumar, an AI/ML Engineer with over 4 years of experience in developing scalable machine learning models and intelligent recommendation systems. I specialize in the full machine learning lifecycle, including data preprocessing, model training, deployment, and monitoring, with a strong background in Python, PyTorch, and applied statistics. I have worked extensively on projects involving credit risk assessment, fraud detection, personalized user engagement, and real-time ML inference pipelines. I am passionate about leveraging AI/ML to deliver business value and enjoy collaborating with cross-functional teams to align AI solutions with organizational goals.…Hi, I'm Pavan Kumar, an AI/ML Engineer with over 4 years of experience in developing scalable machine learning models and intelligent recommendation systems. I specialize in the full machine learning lifecycle, including data preprocessing, model training, deployment, and monitoring, with a strong background in Python, PyTorch, and applied statistics. I have worked extensively on projects involving credit risk assessment, fraud detection, personalized user engagement, and real-time ML inference pipelines. I am passionate about leveraging AI/ML to deliver business value and enjoy collaborating with cross-functional teams to align AI solutions with organizational goals.

Available to hire

Hi, I’m Pavan Kumar, an AI/ML Engineer with over 4 years of experience in developing scalable machine learning models and intelligent recommendation systems. I specialize in the full machine learning lifecycle, including data preprocessing, model training, deployment, and monitoring, with a strong background in Python, PyTorch, and applied statistics.

I have worked extensively on projects involving credit risk assessment, fraud detection, personalized user engagement, and real-time ML inference pipelines. I am passionate about leveraging AI/ML to deliver business value and enjoy collaborating with cross-functional teams to align AI solutions with organizational goals.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

AI-ML Engineer at Zhang Financial

June 1, 2025 - Present

Developed and optimized machine learning models for credit risk assessment, fraud detection, and customer behavior prediction. Engineered features from large, diverse financial datasets to enhance model performance. Deployed scalable ML models using cloud platforms such as AWS and Azure, integrating them with existing financial systems. Monitored model accuracy and drift, retraining models regularly to adapt to financial changes. Collaborated with interdisciplinary teams to align AI solutions with business goals while ensuring compliance with financial regulations through model explainability and audit trails. Automated data pipelines and ML workflows leveraging Apache Airflow and CI/CD best practices. Kept abreast of emerging AI/ML fintech trends to improve financial models.

AI-ML Engineer at Central Michigan University

May 1, 2025 - August 21, 2025

Designed and deployed real-time ML inference pipelines on AWS processing over 10,000 video frames per hour. Developed secure RESTful ML APIs integrated with PostgreSQL and IAM authentication, enhancing data security by 30%. Improved model training and inference throughput by 45% through autoscaling, caching, and network optimizations. Implemented MLflow and Airflow for experiment tracking and automated model retraining. Automated infrastructure provisioning with Terraform and Ansible. Created modular ML microservices to facilitate collaborative development and scalable deployments. Streamlined CI/CD pipelines with Docker and GitHub Actions for zero-downtime releases. Established real-time monitoring and drift detection using AWS CloudWatch to ensure model reliability.

Machine Learning Engineer at Lance Soft Engineering

September 1, 2023 - August 21, 2025

Delivered personalization ML systems using AWS SageMaker, EC2, and XGBoost that boosted user engagement metrics. Designed high-availability ML inference APIs with network load balancing and secure VPC isolation processing millions of requests. Enforced least-privilege IAM policies to protect sensitive data in ML workflows. Automated ML infrastructure provisioning with Terraform and Ansible for scalable and reproducible experiments. Developed CI/CD pipelines with Jenkins and GitHub incorporating rollback and blue/green deployments. Automated complex feature engineering and data orchestration with Python and Shell ETL scripts. Containerized ML workflows using Docker and deployed to Kubernetes clusters with auto-scaling and self-healing capabilities. Established monitoring solutions with CloudWatch and Nagios for anomaly detection and modelhealth. Enabled A/B and canary deployments for safe iterative rollouts. Collaborated with cross-functional Agile teams to align ML outputs with busines

Machine Learning Intern at Carta

March 1, 2020 - August 21, 2025

Assisted in preprocessing and cleaning large datasets for ML model training. Implemented basic machine learning algorithms including linear regression, decision trees, and clustering. Conducted exploratory data analysis to identify patterns and relevant features. Supported model evaluation using accuracy, precision, and recall metrics. Collaborated with team members to fine-tune model parameters and improve performance. Documented experimental results and prepared presentations for project updates.

AI-ML Engineer at Zhang Financial

June 1, 2025 - Present

Developed and optimized machine learning models for credit risk assessment, fraud detection, and customer behavior prediction. Preprocessed and engineered features from large, diverse financial datasets to improve model performance. Deployed scalable ML models on cloud platforms like AWS and Azure, integrated with existing financial systems. Monitored model accuracy and detected drift, retraining models regularly to adapt to financial changes. Collaborated with data scientists, engineers, and business teams to align AI solutions with financial goals and ensured compliance with financial regulations. Automated data pipelines and ML workflows using Apache Airflow and CI/CD practices. Stayed updated with emerging AI/ML techniques and fintech trends to continuously improve models.

AI-ML Engineer at Central Michigan University

May 31, 2025 - August 21, 2025

Designed and deployed real-time ML inference pipelines on AWS handling high-volume video frames. Developed secure RESTful ML APIs with PostgreSQL and IAM authentication, improving data security by 30%. Improved training and inference throughput by 45% using autoscaling, caching, and network optimizations. Implemented MLflow and Airflow for experiment tracking and automated model retraining. Automated infrastructure provisioning with Terraform and Ansible for consistent environments. Created modular ML microservices for collaborative development and scalable deployments. Streamlined CI/CD pipelines with Docker and GitHub Actions for zero-downtime releases. Set up real-time monitoring and drift detection with AWS CloudWatch to ensure model reliability.

Machine Learning Engineer at Lance Soft Engineering

September 30, 2023 - August 21, 2025

Delivered personalization ML systems using AWS SageMaker, EC2, and XGBoost, which boosted user engagement metrics across client applications. Designed high-availability ML inference APIs with NLB/ALB routing and secure VPC isolation, serving millions of requests. Enforced least-privilege IAM policies protecting sensitive data throughout ML workflows. Automated ML infrastructure provisioning using Terraform and Ansible, ensuring scalable and reproducible experiments. Developed Jenkins/GitHub CI/CD pipelines supporting rollback and blue/green deployments for high availability. Created Python and Shell ETL automation scripts for complex feature engineering and multi-format data orchestration. Containerized ML workflows with Docker and deployed on Kubernetes clusters with auto-scaling and self-healing. Established proactive monitoring using CloudWatch and Nagios for anomaly and degradation detection. Enabled A/B and canary deployments for safe, iterative rollouts. Collaborated with cross-f

Machine Learning Intern at Carta

March 31, 2020 - August 21, 2025

Assisted in preprocessing and cleaning large datasets for training ML models. Implemented basic machine learning algorithms including linear regression, decision trees, and clustering. Conducted exploratory data analysis to identify patterns and relevant features. Supported model evaluation using accuracy, precision, and recall metrics. Collaborated with team members to fine-tune model parameters and improve performance. Documented experimental results and prepared presentations for project updates.