• Design end-to-end data and AI architectures across cloud and hybrid environments (e.g., Azure, AWS, GCP). • Define data and model lifecycle management frameworks that support ingestion, transformation, storage, and consumption at scale. • Establish and enforce governance, security, and compliance standards for AI and data usage. • Partner with stakeholders in Agile planning sessions, sprint reviews, and backlog prioritization to translate business goals into technical architectures and roadmaps. • Develop robust, modular data pipelines using tools such as Databricks, Spark, Airflow, or Synapse. • Implement Agile-friendly CI/CD frameworks for data and ML workloads to support incremental delivery and continuous improvement. • Optimize systems for real-time analytics, batch processing, and ML training pipelines. • Lead the adoption of data lakehouse and semantic layer architectures to unify structured and unstructured data sources. • Architect scalable solutions for machine learning, generative AI, and predictive analytics, from experimentation to production deployment. • Integrate MLOps and Agile DevOps practices for model lifecycle management, leveraging tools like MLflow, Kubeflow, Vertex AI, or Azure ML. • Oversee model governance, explainability, and performance monitoring frameworks. • Collaborate with cross-functional teams in Agile sprints to experiment, validate, and iterate on AI prototypes. • Champion Agile and DevOps methodologies, promoting iterative development, continuous learning, and feedback-driven improvement. • Mentor engineers, data scientists, and analysts in modern AI and data architecture practices. • Collaborate closely with product owners, Scrum Masters, and stakeholders to align AI initiatives with business value creation. • Stay ahead of emerging trends in data platforms, AI frameworks, and responsible AI governance.