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I'm an AI/ML Engineer with 3 years of hands-on experience designing, deploying, and optimizing machine learning systems, focused on fraud detection, computer vision, MLOps, and generative AI across enterprise and FinTech domains. I enjoy turning complex data into scalable production solutions on cloud platforms, collaborating with cross-functional teams, and sharing knowledge through documentation and demos. I thrive on building reliable ML pipelines, monitoring model drift, and delivering measurable business impact.…I'm an AI/ML Engineer with 3 years of hands-on experience designing, deploying, and optimizing machine learning systems, focused on fraud detection, computer vision, MLOps, and generative AI across enterprise and FinTech domains. I enjoy turning complex data into scalable production solutions on cloud platforms, collaborating with cross-functional teams, and sharing knowledge through documentation and demos. I thrive on building reliable ML pipelines, monitoring model drift, and delivering measurable business impact.

Sowmya Raavi

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I'm an AI/ML Engineer with 3 years of hands-on experience designing, deploying, and optimizing machine learning systems, focused on fraud detection, computer vision, MLOps, and generative AI across enterprise and FinTech domains. I enjoy turning complex data into scalable production solutions on cloud platforms, collaborating with cross-functional teams, and sharing knowledge through documentation and demos. I thrive on building reliable ML pipelines, monitoring model drift, and delivering measurable business impact.…I'm an AI/ML Engineer with 3 years of hands-on experience designing, deploying, and optimizing machine learning systems, focused on fraud detection, computer vision, MLOps, and generative AI across enterprise and FinTech domains. I enjoy turning complex data into scalable production solutions on cloud platforms, collaborating with cross-functional teams, and sharing knowledge through documentation and demos. I thrive on building reliable ML pipelines, monitoring model drift, and delivering measurable business impact.

Available to hire

I’m an AI/ML Engineer with 3 years of hands-on experience designing, deploying, and optimizing machine learning systems, focused on fraud detection, computer vision, MLOps, and generative AI across enterprise and FinTech domains.

I enjoy turning complex data into scalable production solutions on cloud platforms, collaborating with cross-functional teams, and sharing knowledge through documentation and demos. I thrive on building reliable ML pipelines, monitoring model drift, and delivering measurable business impact.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

Javanese

Advanced

Bashkir

Advanced

English

Fluent

Work Experience

AI/ML Engineer at MasterCard

November 1, 2024 - Present

Designed and deployed scalable machine learning models including XGBoost, Random Forest, and Deep Neural Networks for fraud detection and predictive analytics using AWS SageMaker, which improved recall by 42% across over 500 million transactions. Built real-time ML pipelines with AWS Step Functions, Lambda, and Spark for multi-terabyte data ingestion and low-latency inference. Developed NLP models with BERT and GPT to automate document classification, KYC processes, and chatbot applications, decreasing manual processing efforts by 60%. Implemented MLOps workflows with MLflow, Docker, and Apache Airflow to automate training and deployment, reducing model drift and ensuring CI/CD reliability. Created time-series forecasting models (ARIMA, LSTM) to predict transaction volumes, reducing forecasting error by 20%. Developed dashboards and monitoring tools using Tableau, Grafana, and Weights & Biases for anomaly detection and model explainability. Collaborated cross-functionally to align AI s

AI/ML Engineer at Dell Technologies

June 1, 2023 - August 25, 2025

Developed a supervised ticket-routing recommender system using Random Forest and SVM, automating 80% of support ticket classification and reducing resolution time by 30%. Architected a vision-based QA system using YOLOv4 and OpenCV for real-time hardware inspection achieving 92% defect detection accuracy. Designed an anomaly detection engine for system logs using autoencoders and Isolation Forest able to detect 40% of failures before outages. Integrated Azure ML pipelines and GitHub Actions CI/CD to deploy new model versions weekly with zero downtime. Collaborated with product managers to convert AI use cases into standardized tools, increasing operational efficiency by 15%. Established continuous training pipelines using MLflow and Airflow to automate retraining and ensure version tracking.

AI/ML Engineer at MasterCard

November 1, 2024 - Present

Designed and deployed scalable machine learning models for fraud detection and predictive analytics using AWS SageMaker, improving recall by 42% over 500M+ transactions. Built and maintained real-time ML pipelines for multi-terabyte datasets with AWS Step Functions, Lambda, and Spark. Developed and fine-tuned NLP models like BERT and GPT to automate KYC and chatbot applications, cutting manual effort by 60%. Implemented MLOps workflows with MLflow, Docker, and Apache Airflow for automated training, testing, and deployment, reducing model drift and ensuring CI/CD with GitHub Actions and Jenkins. Created time-series forecasting models to predict transaction volumes, reducing forecast error by 20%. Developed dashboards using Tableau, Grafana, and Weights & Biases for real-time anomaly detection and model explainability. Collaborated cross-functionally ensuring governance and compliance with automated drift detection and version control.

AI/ML Engineer at Dell Technologies

June 30, 2023 - August 25, 2025

Developed a supervised ticket-routing recommender automating 80% of support ticket classification and reducing resolution time by 30%. Architected a vision-based QA system using YOLOv4 and OpenCV achieving 92% defect detection accuracy for real-time hardware inspection. Designed an anomaly detection engine leveraging autoencoders and Isolation Forest detecting 40% of failures before outages. Integrated Azure ML pipelines with GitHub Action CI/CD to enable weekly zero-downtime model deployments. Collaborated with product managers to standardize AI use cases into operational tools, boosting efficiency by 15%. Established continuous training pipelines with MLflow and Airflow to automate model retraining and version tracking.

AI/ML Engineer at MasterCard, USA

November 1, 2024 - Present

Designed and deployed scalable ML models (XGBoost, Random Forest, Deep Neural Networks) for fraud detection and predictive analytics using AWS SageMaker, achieving a 42% improvement in recall across 500M+ transactions. Built real-time ML pipelines with AWS Step Functions, Lambda, and Spark to ingest, preprocess, and serve multi-terabyte datasets within 10 minutes of generation, supporting both batch and low-latency inference. Developed and fine-tuned NLP models including BERT and GPT for document classification, KYC automation, and chatbot applications, reducing manual processing effort by 60%. Implemented robust MLOps workflows using Python, MLflow, Docker, and Apache Airflow to automate training, testing, deployment, and monitoring, reducing model drift and ensuring reliable CI/CD with GitHub Actions and Jenkins. Created time-series forecasting models (ARIMA, LSTM) to predict transaction volumes, enabling proactive resource planning and reducing forecasting error by 20%. Developed in

AI/ML Engineer at Dell Technologies, India

June 1, 2023 - October 15, 2025

Developed supervised ticket-routing recommender (RF/SVM) automating 80% of support ticket classification and decreasing resolution time by 30%. Architected vision-based QA system using YOLOv4/OpenCV for real-time hardware inspection achieving 92% defect detection accuracy. Designed anomaly detection engine for system logs using autoencoders and Isolation Forest; detected 40% of failures prior to outage. Integrated Azure ML pipelines and GitHub Action CI/CD to deploy new model versions weekly with zero downtime. Collaborated with PMs to convert AI use cases into standardized tools, boosting operational efficiency by 15%. Established a continuous training pipeline using MLflow and Airflow, ensuring automated model retraining with updated data inputs and version tracking.

AI/ML Engineer at MasterCard, USA

November 1, 2024 - November 6, 2025

Designed and deployed scalable ML models (XGBoost, Random Forest, Deep Neural Networks) for fraud detection and predictive analytics on 500M+ transactions using AWS SageMaker; achieved a 42% improvement in recall. Built real-time ML pipelines using AWS Step Functions, Lambda, and Spark to ingest, preprocess, and serve multi-terabyte datasets with sub-10-minute latency, supporting both batch and low-latency inference. Fine-tuned NLP models (BERT, GPT) for document classification, KYC automation, and chatbot applications, reducing manual processing by 60%. Implemented robust MLOps workflows with Python, MLflow, Docker, and Apache Airflow to automate training, testing, deployment, and monitoring, reducing model drift and enabling CI/CD via GitHub Actions. Developed time-series forecasting models (ARIMA, LSTM) to predict transaction volumes, enabling proactive resource planning and reducing forecasting error by 20%. Built interactive dashboards with Tableau, Grafana, and Weights & Biases f

AI/ML Engineer at Dell Technologies, India

June 1, 2023 - June 1, 2023

Developed supervised ticket-routing recommender (RF/SVM) automating 80% of support ticket classification and cutting resolution time by 30%. Architected a vision-based QA system using YOLOv4/OpenCV for real-time hardware inspection achieving 92% defect detection accuracy. Designed anomaly detection engine for system logs using autoencoders and Isolation Forest; detected 40% of failures prior to outages. Integrated Azure ML pipelines with GitHub Actions for weekly model version deployment with zero downtime. Collaborated with PMs to convert AI use cases into standardized tools, boosting operational efficiency by 15%. Established a continuous training pipeline using MLflow and Airflow, ensuring automated retraining with updated data and version tracking.

Education

MS in Computer Science at Texas Tech University

August 1, 2023 - May 1, 2025

B.Tech in Computer Science and Engineering at Hindustan Institute of Tech & Science, Chennai, India

July 1, 2017 - May 1, 2021

MS in Computer Science at Texas Tech University

August 1, 2023 - May 1, 2025

B.Tech in Computer Science and Engineering at Hindustan Institute of Tech & Science

July 1, 2017 - May 1, 2021

MS in Computer Science at Texas Tech University, Lubbock, TX

August 1, 2023 - May 1, 2025

B.Tech in CSE at Hindustan Institute of Technology & Science, Chennai, India

July 1, 2017 - May 1, 2021

MS in Computer Science at Texas Tech University

August 1, 2023 - May 1, 2025

B.Tech in CSE at Hindustan Institute of Tech & Science, Chennai, India

July 1, 2017 - May 1, 2021