Innovation Engineer

Overview

Develop agentic AI systems for water infrastructure, forecasting, and operations.

Key Responsibilities

• mlops
• model tracking
• azure devops
• agentic ai
• llm integration
• prototype development

Tasks

-Support the MLOps lifecycle, including model tracking (e.g., MLflow), deployment, and monitoring within Azure DevOps and Databricks environments. -Design and implement agentic AI systems, including singleagent and multiagent 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. -Document methodologies, share learnings, and contribute to internal knowledge bases and Communities of Practice. -Translate domainspecific challenges into scalable AI solutions, working closely with water engineers and project teams. -Contribute to the development of exploratory prototypes and productionready tools that leverage agentic reasoning, planning, and collaboration. -Stay current with emerging research and tools in Machine Learning, agentic AI, reinforcement learning, and multiagent systems.

Requirements

• communication
• python
• tensorflow
• azure
• langchain
• master's

What You Bring

-Strong communication skills and ability to collaborate across technical and nontechnical teams. -Strong programming skills with Python, PyTorch or TensorFlow, and cloudbased ML platforms (preferably Azure). Skills in multiple languages (e.g., Java, Node.js) is a benefit. -Experience or interest in water systems, environmental modeling, or Microsoft Azure SDKs such as AI Foundry is a plus. -Master’s+ degree in Artificial Intelligence, Computer Science, Data Science, or a related field. -Familiarity with LLM frameworks (e.g., LangChain, AutoGen, OpenAI APIs) and multiagent orchestration tools. Handson experience building AI agents with LLMs, including RAG, using these frameworks is preferred. -5+ years total professional experience, 1+ of handson experience in machine learning and AI development, with exposure to agentic AI concepts (e.g., autonomous agents, multiagent coordination, LLMbased agents). -Understanding of MLOps practices, including CI/CD pipelines, model versioning, and monitoring.

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About Stantec

-Began as a one-person environmental engineering practice and has grown through strategic geographic and service expansion. -Publicly listed on the NYSE and TSX, Stantec has completed major acquisitions to bolster expertise and strengthen global infrastructure portfolio. -Headquartered in the iconic Stantec Tower in Edmonton, the company has delivered landmark projects like the Panama Canal Expansion and high-profile community developments. -Leverages interdisciplinary teams of designers, engineers, scientists, and project managers. -Typical projects span urban planning, civil infrastructure, water management, environmental assessment, energy delivery, and architectural design, serving public and private sectors. -Blends technical prowess with visionary design in bold ventures such as designing its own mixed-use tower and leading multi-nation infrastructure projects.

Sector Specialisms