Dynamic and results-oriented Azure DataOps and MLOps Architect with over 13+ years of IT experience, including 8+ years in technical leadership roles. Proven expertise in Azure Data Engineering, Application Development, MLOps, and LLMOps, with a strong track record of building and leading high-performing cloud data engineering teams.
Currently leading an Azure Data Engineering team at Capgemini Invent, driving innovation and operational excellence in cloud-native data solutions. Adept at designing and developing ETL pipelines, data models, and data architectures using both on-premises and cloud-based tools.
Skilled in:
Azure Data Factory (pipelines, datasets, linked services, integration runtime, custom activities using C#)
Azure Databricks (PySpark, Spark job orchestration, ADLS Gen2 integration)
Azure Storage Solutions (Blob Storage, Data Lake Store, Cosmos DB, Storage Tables)
MLOps/LLMOps using Azure ML Studio, mlflow, PyTorch, TensorFlow, pandas, Azure DevOps, Kubernetes, and Docker Swarm
On-premises tools: SSIS, SSAS, REST APIs, Power BI
Big Data: Hadoop, Hive QL
Agile/Scrum methodologies
Experienced in building CI/CD pipelines, deploying machine learning models, and implementing data quality frameworks using C# and Hive QL. Passionate about fostering innovation, collaboration, and continuous improvement in cloud data engineering environments.
Data Warehouse, Data Modeling, Data Visualization
Azure Data Factory, ADLS, Azure Databricks, Function App, Logic App
MLOps, Model training, Model Evaluation, Model deployment, LLMOps
Integration Architecture, Azure Data Engineering, Application Development, Microsoft Azure
Azure Machine Learning Studio
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