Deployed MLflow to track experiments and streamline the model lifecycle for AI-driven projects, ensuring reproducibility and efficient management of model artifacts, from experiment to deployment.Leveraged DVC for versioning machine learning pipelines and data, while using MLflow to track model versions and experiments, all integrated within Azure ML Studio to enhance traceability and collaboration.Improved consumer satisfaction by 25% by deploying a personalised energy-saving recommendation system using Azure ML Studio and fine-tuned LLMs for tailored advice.Integrated Apache Airflow to automate workflow scheduling and management for LLMOps projects, improving operational efficiency by 40%.

Developed a predictive maintenance system using AWS SageMaker, reducing maintenance costs by 20% and minimising flight delays by 35%.Deployed machine learning models using Kubeflow on Kubernetes, improving model scalability and deployment speeds across cloud environments.Integrated GitHub Actions for CI/CD workflows, automating testing and model deployment pipelines, reducing manual effort by 30%.

Automated data workflows using Apache Airflow, reducing manual ETL effort by 25%, and improving accuracy in financial risk predictions using Azure DevOps pipelines.Built machine learning models using Scikit-learn and deployed them via Docker containers, achieving an 83% model accuracy rate in loan approval predictions.

Reduced release times by 50% by building a CI/CD pipeline with Azure DevOps, automating the deployment of machine learning models across Docker containers.Streamlined deployment processes for microservices architecture using Kubernetes, enabling continuous delivery for AI models in production.

• Engineered robust ETL pipelines using Azure Data Factory and Azure Databricks, reducing data processing time by 40% for the Cloud Contact Next Generation (CCNG) product used by customer contact centres.• Implemented data lake architecture using Azure Synapse Analytics, improving data accessibility and enabling real-time analytics for contact centre operations.• Developed interactive Power BI dashboards and reports, providing actionable insights that led to a 25% improvement in customer satisfaction metrics.• Implemented data quality checks and data governance practices, ensuring 99.9% data accuracy across the CCNG ecosystem.• Skills Used: Azure Databricks, Azure Data Factory, Azure Synapse Analytics, SQL Server, Python, PySpark, Power BI, Pandas, Data Modelling, ETL/ELT, Data Governance

Led a team that delivered high-performance web applications using .Net Core API and Angular, deployed on Azure WebApp, ensuring seamless service delivery.Optimised microservices architecture by integrating design patterns such as Saga, CQRS, and event sourcing, improving system scalability and reliability.