Building scalable AI systems, ML pipelines, and cloud-native platforms.
I am an AI Systems Engineer with 4+ years of experience across Data Analytics, DevOps, and enterprise systems.
I started my career working on banking systems using Mainframes (z/OS, JCL, Control-M), managing large-scale data processing and operations.
I then moved into Data Analytics and Business Intelligence, building dashboards and ETL pipelines using SQL, Power BI, and Azure.
Currently, I specialize in building AI systems, including MLOps pipelines, LLM applications, and cloud-native deployments using Kubernetes, Terraform, and CI/CD pipelines.
My strength is combining enterprise systems, data engineering, and AI into scalable production solutions.
Designed and deployed a Retrieval-Augmented Generation system using LangChain, FAISS/Pinecone, and FastAPI. Integrated LLMs for intelligent responses with scalable deployment on Kubernetes.
Built end-to-end MLOps pipelines using Kubeflow and MLflow with automated training, versioning, deployment, and monitoring.
Designed scalable AI infrastructure using AWS (EKS), Azure (AKS), Terraform, Docker, and CI/CD pipelines.
Built business dashboards using Power BI, SQL, and Azure Data Factory for real-time analytics and reporting.
Managed banking systems using Control-M, JCL, and COBOL, including job scheduling, ETL workflows, and production support.
AI/ML: LLMs, RAG, Transformers
MLOps: Kubeflow, MLflow, Airflow
Cloud: AWS, Azure
DevOps: Kubernetes, Docker, Terraform
Data: SQL, Power BI, ETL