AI Analytics + Azure DevOps

As an experienced AI Analytics Engineer with expertise in Azure DevOps, your role will involve designing, implementing, and optimizing data pipelines, machine learning models, and analytics solutions. You will be responsible for bridging the gap between data science, engineering, and DevOps practices to deliver scalable and production-ready AI/ML solutions. Key Responsibilities: - Design, develop, and deploy AI/ML models and analytics workflows. - Build and manage end-to-end CI/CD pipelines in Azure DevOps for data and ML projects. - Automate data ingestion, preprocessing, model training, testing, and deployment. - Monitor model performance and implement retraining pipelines. - Work closely with data scientists, data engineers, and business stakeholders to translate requirements into scalable solutions. - Ensure solutions are secure, cost-optimized, and highly available on Azure. - Perform root cause analysis and continuous improvement for production issues. Qualifications Required: - Hands-on experience with Azure DevOps (pipelines, repos, artifacts, boards). - Strong programming skills in Python or R for AI/ML development. - Experience with Azure Machine Learning, Databricks, Synapse, Data Factory. - Good understanding of MLOps principles and tools. - Strong knowledge of data visualization and analytics (Power BI a plus). - Familiarity with containerization (Docker, Kubernetes) for deploying ML workloads. - Experience with version control (Git) and agile development practices. In addition to the above details, the job description also emphasizes the importance of the following soft skills: - Excellent communication and collaboration skills. - Ability to translate technical insights into business value. - Strong analytical thinking and attention to detail. (Note: Any additional details of the company were not provided in the job description),