A Seasoned AI/ML and Full Stack Data professional his skills in Data Science, Machine Learning, Artificial Intelligence, Embedded AI, Distributed Computing and Constructive Visualisation.
Recent Projects
Project-1 : Driver Behaviour Analytics System
Aim: Built comprehensive driver risk analytics system using survival analysis and Bayesian modeling for 300K+ drivers with real-time assessment capabilities.
Key Metrics: 88% churn prediction accuracy, 40% reduced claim processing time, 10K+ daily API requests with < 200ms response time.
Tech Stack: Apache Kafka, AWS S3, PyMC3, FastAPI, PostgreSQL. Integrated Bridgestone ecosystem data with Cox regression and MCMC sampling for production deployment.
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Key Metrics: 88% churn prediction accuracy, 40% reduced claim processing time, 10K+ daily API requests with < 200ms response time.
Tech Stack: Apache Kafka, AWS S3, PyMC3, FastAPI, PostgreSQL. Integrated Bridgestone ecosystem data with Cox regression and MCMC sampling for production deployment.
Project-2 : Production LLM Serving Optimization Framework
Aim: High-performance LLM serving framework with vLLM continuous batching, INT8/INT4 quantization, and multi-GPU tensor parallelism for enterprise AI applications.
Key Metrics: 12.3K+ requests/sec throughput, 42ms P50 latency, 70% memory reduction, 1500+ concurrent users, 2.3x speedup with custom CUDA kernels.
Tech Stack: vLLM, FastAPI, Docker/Kubernetes, Prometheus/Grafana. Implemented distributed inference with token streaming and production monitoring.
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Key Metrics: 12.3K+ requests/sec throughput, 42ms P50 latency, 70% memory reduction, 1500+ concurrent users, 2.3x speedup with custom CUDA kernels.
Tech Stack: vLLM, FastAPI, Docker/Kubernetes, Prometheus/Grafana. Implemented distributed inference with token streaming and production monitoring.