At Pulse, I improved machine learning forecasting performance by implementing soft-gating and optimized end-to-end pipelines for energy demand forecasting.
I also established model validation and monitoring frameworks to track performance and detect drift, and integrated ML, simulation, and optimization systems for intelligent energy management.
I’ve built LLM-powered applications and RAG pipelines, and developed ML observability systems including a platform for real-time drift detection.
I’ve also worked on an end-to-end predictive analytics system with automated retraining and CI/CD. My focus is taking AI/ML models from experimentation to scalable, production-ready deployment, with attention to monitoring, reliability, and continuous improvement.
