• Developed Transformer-based recommendation models using PyTorch/TensorFlow and applied model distillation to optimize inference performance, improving recommendation accuracy by 14% while increasing customer engagement by 12%.

• Engineered LangChain RAG pipelines with Pinecone vector databases and implemented LLM evaluation frameworks, enhancing query relevance by 19% and improving enterprise knowledge retrieval accuracy across internal systems.

• Designed time-series forecasting models in TensorFlow/Prophet, reducing stock-out frequency by 18%, improving warehousedistribution accuracy, and optimizing retail demand planning workflows.

• Implemented real-time Kafka pipelines orchestrated with Airflow, decreasing reporting latency by 42%, enabling timely analytics delivery for supply chain monitoring systems.

• Introduced A/B testing frameworks for ML validation, improving performance reliability by 11% while ensuring statistically valid deployment of production-ready machine learning models.

• Deployed monitoring with MLflow + Prometheus, enabling drift detection, tracking latency metrics, and ensuring SLA compliance across enterprise-level machine learning deployments.

• Built churn prediction frameworksin PyTorch/TensorFlow,reducing attrition by 12% acrosstelecom and retail portfolios, improving longterm customer retention strategies.

• Migrated machine learning workflows to Amazon Web Services (AWS), lowering latency by 31%, achieving scalability improvements and sustaining production-ready throughput for critical ML applications.

• Configured Spark/Hadoop clustersfor distributed training, reducing dataset preprocessing time from six hoursto two, accelerating largescale training efficiency significantly.

• Applied Hugging Face Transformers to unstructured review data, improving sentiment classification accuracy 16% compared to baseline systems, ensuring reliable text classification at production scale.

• Automated deployment pipelines with Kubernetes, reducing failed releases 40%, improving reproducibility, and stabilizing production ML workflows across multi-tenant enterprise environments.

• Built anomaly detection monitoring with Grafana + ELK Stack, ensuring SLA adherence and supporting predictive maintenance for multitenant enterprise production ML pipelines.