Job Summary (MLOps Developer) • Led evolution of data-focused projects from Data Engineering/Data Science into advanced MLOps roles. • Spent 80% of time on hands-on development and 20% on architectural tasks under senior architects, acquiring practical architecture skills. • Over 7 years of Python and 3 years of TypeScript development experience. • Documented architecture, workflows, and best practices for knowledge sharing and compliance. • Provided technical oversight and established guidelines for team members. • Implemented end-to-end MLOps and LLMOps pipelines using Azure Machine Learning and Azure OpenAI. • Designed scalable infrastructure for training, deployment, and monitoring of ML/LLM models in production environments. • Collaborated with data scientists and engineers to optimize model development, testing, and deployment processes. • Managed Azure Kubernetes Service (AKS) clusters and containerized machine learning workloads. • Ensured model governance, versioning, and reproducibility using MLflow and Azure DevOps. • Advocated for and integrated DevSecOps practices to ensure security and compliance throughout the ML lifecycle. • Monitored, troubleshot, and maintained production ML systems to guarantee high availability and performance. • Demonstrated experience with Azure Machine Learning, Azure OpenAI, Azure DevOps, and AKS. • Proficient in Python, Docker, Kubernetes, and CI/CD pipeline implementation. • Experienced in LLM fine-tuning, prompt engineering, and model deployment. • Familiar with MLflow, Terraform, and monitoring tools such as Prometheus/Grafana.