Your OpportunityWe 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.
QualificationsQualifications- Master's+ degree in Artificial Intelligence, Computer Science, Data Science, or a related field.
- 5+ years of total professional experience, 1+ of hands-on experience in machine learning and AI development, with exposure to agentic AI concepts (e.g., autonomous agents, multi-agent coordination, LLM-based agents).
- Familiarity with LLM frameworks (e.g., LangChain, AutoGen, OpenAI APIs) and multi-agent orchestration tools. Hands-on experience building AI agents with LLMs, including RAG, using these frameworks is preferred.
- Strong programming skills with Python, PyTorch or TensorFlow, and cloud-based ML platforms (preferably Azure). Skills in multiple languages (e.g., Java, Node.js) is a benefit.
- Understanding of MLOps practices, including CI/CD pipelines, model versioning, and monitoring.
- Strong communication skills and ability to collaborate across technical and non-technical teams.
- Experience or interest in water systems, environmental modeling, or Microsoft Azure SDKs such as AI Foundry is a plus.
About the TeamPay Transparency: In compliance with pay transparency laws, pay ranges are provided for positions in locations where required. Please note, the final agreed upon compensation is based on individual education, qualifications, experience, and work location. At Stantec certain roles are bonus eligible. Actual compensation for part-time roles will be pro-rated based on the agreed number of working hours per week.
Benefits Summary: Regular full-time and part-time employees (working at least 20 hours per week) will have access to health, dental, and vision plans, a wellness program, health care spending account, wellness spending account, group registered retirement savings plan, employee stock purchase program, group tax-free savings account, life and accidental death & dismemberment (AD&D) insurance, short-term/long-term disability plans, emergency travel benefits, tuition reimbursement, professional membership fee coverage, and paid time off.
Temporary/casual employees will have access to group registered retirement savings plan, employee stock purchase program, and group tax-free savings account.
The benefits information listed above may not apply to union positions because benefits for such positions are governed by applicable collective bargaining agreements.
