Innovation Engineer

Communities everywhere share the need for clean water. And as they work to meet that need, they’re facing new, more complex challenges every day. Our water professionals collaborate to contribute innovative ideas, guided by scientific rigor and a vision for sustainable growth. Every day, we’re helping communities improve, reuse, and protect our world’s most precious resource.

Join our team to deliver transformational projects that will benefit future generations. Grow with us, H2O+U.

Your Opportunity

We are seeking a forward-thinking ML/AI Engineer with a strong foundation in AI and a growing specialization in agentic AI, including both single-agent and multi-agent systems. This role will support our expanding portfolio of AI-driven solutions in the water sector, contributing to a range of projects that involve complex systems, data-driven decision-making, and intelligent automation.

You will collaborate with a cross-functional team of data scientists, engineers, and domain experts to design, prototype, and deploy agentic AI solutions that enhance operational efficiency, system adaptability, and innovation across diverse water-related challenges. You will report to an Innovation Portfolio Manager in Stantec Water BOU’s Office of Research, Innovation and Technology.

Key Responsibilities

• 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.