I design, build, and deploy production-grade ML applications and inference systems, and architect scalable MLOps platforms with a focus on security and operational excellence.
Foundation in building structured systems, maintainable codebases, APIs, and delivery workflows.
Experience in experimentation, modelling, feature design, and turning business problems into measurable ML outcomes.
Current focus on deploying, scaling, monitoring, and governing ML systems in production environments.
Production-grade blueprint for deploying and operating scalable ML inference workloads on Kubernetes and OpenShift, leveraging NVIDIA Triton Inference Server.
CI/CD design for training, validation, model registry, deployment, and monitoring.
LLM application with retrieval, filtering, observability, and secure logging controls.
System design basics what a beginner should know.
Read article →A practical starter guide to monitoring ML workloads with Prometheus on OpenShift, covering metrics collection, inference reliability, and observability basics.
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My focus is not only on building models, but on designing reliable, scalable, and observable AI systems that operate effectively in real-world environments.