• Design and implement agentic AI systems, including single-agent and multi-agent architectures, for applications in water infrastructure, forecasting, optimizations, and operations. • Collaborate with ML/AI leads to integrate LLMs and autonomous agents into existing and new solution pipelines. • Contribute to the development of exploratory prototypes and production-ready tools that leverage agentic reasoning, planning, and collaboration. • Support the MLOps lifecycle, including model tracking (e.g., MLflow), deployment, and monitoring within Azure DevOps and Databricks environments. • Translate domain-specific challenges into scalable AI solutions, working closely with water engineers and project teams. • Stay current with emerging research and tools in Machine Learning, agentic AI, reinforcement learning, and multi-agent systems. • Document methodologies, share learnings, and contribute to internal knowledge bases and Communities of Practice.