Open to opportunities

Ganesh Vathyaram

@ganeshvathyaram

Message

AI & ML Engineer building low-latency, large-scale ML systems for ads and enterprise platforms.

United States

Message

What I'm looking for

I’m looking to keep building production-grade ML systems—end-to-end pipelines, low-latency ranking/recommendations, and MLOps with monitoring and A/B testing—on large-scale data where I can improve inference latency and control cloud/compute costs.

I’ve designed and deployed end-to-end machine learning pipelines at Yahoo Ads, using Python, Apache Spark, and Dask to process petabyte-scale ad event data and speed up feature engineering and model training.

I’ve built and optimized real-time bidding and predictive models with XGBoost/LightGBM and deep learning to improve CTR prediction accuracy, and I’ve used LLMs with RAG for audience segmentation, contextual targeting, and recommendation systems.

At Yahoo Ads and Capgemini, I’ve also worked across model training, deployment, monitoring, and experimentation—shipping low-latency ranking and recommendation services, improving inference latency and compute cost efficiency with GPU/TensorRT, and implementing MLOps with MLflow.

Experience

Work history, roles, and key accomplishments

Current

AI & ML Engineer

Current

Yahoo Ads

Apr 2025 - Present (1 year 4 months)

Designed and deployed end-to-end ML pipelines processing petabyte-scale ad event data, reducing feature engineering latency by 30%. Built and optimized RTB and predictive models improving CTR prediction accuracy by 6-9%.

Python Apache Spark Dask XGBoost LightGBM Deep Learning PyTorch Kubernetes GPU Acceleration TensorRT MLOps A B Testing

Machine Learning Engineer

Capgemini

Aug 2021 - Jul 2024 (2 years 11 months)

Developed scalable feature engineering and ETL pipelines using Python and Apache Spark, improving training data readiness by 20%. Architected production ML solutions on AWS reducing inference latency by 35%.