Job Description

Headquartered in Toronto, Polar Racking Inc. is a North American leader in the design, engineering and manufacturing of PV mounting systems. We are at the forefront of PV innovation, leading the industry in cutting edge design with a focus on continuous innovation. Our vision is to deliver the highest value to our clients at the lowest cost per kWh. To support this, the company has a diversified portfolio of ground mount and carport products for the commercial and utility scale markets. This, coupled with our unique suite of engineering collaboration tools, technical services and installation support allow us to deliver turnkey solutions that are modular, lighter, and quicker to install. We support our North American and Caribbean clients from our locations in Toronto, Calgary, and US.

We are seeking a talented and highly motivated AI Engineer to join our growing team. This is a unique opportunity to leverage the power of artificial intelligence to revolutionize our internal operations, optimize engineering design processes, and enhance our product development lifecycle. You will play a pivotal role in identifying, developing, and deploying AI-driven solutions that directly impact our efficiency, cost-effectiveness, and innovation capabilities.

Responsibilities:

Identify AI Opportunities: Collaborate with engineering, operations, sales, and other departments to identify pain points and opportunities where AI solutions can significantly improve efficiency, accuracy, and decision-making. Design and Develop AI Models: Lead the design, development, training, and deployment of machine learning and deep learning models for various applications, including but not limited to: o Automated Engineering Design: Develop AI algorithms for optimizing solar mounting system layouts, structural analysis, material selection, and component placement based on site-specific data, weather patterns, and structural integrity requirements. Operational Optimization: Create AI tools for supply chain optimization, inventory management, production scheduling, and logistics to reduce waste and improve turnaround times. Predictive Maintenance: Implement AI models for predicting potential failures in manufacturing equipment or installed systems to enable proactive maintenance. Quality Control: Develop computer vision or other AI techniques for automated quality inspection of manufactured components. Data Analysis & Insights: Build AI tools to extract actionable insights from large datasets related to project performance, customer feedback, and market trends. Data Preprocessing and Feature Engineering: Work with large, complex datasets, performing data cleaning, transformation, and feature engineering to prepare data for model training. Model Deployment and Monitoring: Deploy AI models into production environments and establish robust monitoring systems to track performance, detect drift, and ensure ongoing effectiveness. Research and Innovation: Stay abreast of the latest advancements in AI, machine learning, and relevant industry trends. Propose and experiment with new technologies and methodologies to continuously improve our AI capabilities. Collaboration and Communication: Work closely with cross-functional teams, including software engineers, mechanical engineers, structural engineers, and operations managers, to integrate AI solutions seamlessly into existing workflows and systems. Clearly communicate technical concepts and findings to non-technical stakeholders. Documentation: Maintain comprehensive documentation of AI models, algorithms, data pipelines, and deployment procedures.

Qualifications:

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, Robotics, or a related quantitative field. 3+ years of professional experience as an AI Engineer, Machine Learning Engineer, or Data Scientist with a strong focus on deploying AI solutions. Strong programming skills in Python and experience with relevant AI/ML libraries (e.g., TensorFlow, PyTorch, Keras, scikit-learn, Pandas, NumPy). Solid understanding of various machine learning algorithms (e.g., supervised, unsupervised, reinforcement learning) and deep learning architectures (e.g., CNNs, RNNs, Transformers). Experience working with large datasets, including data cleaning, transformation, and feature engineering. Experience with cloud-based AI/ML platforms (e.g., AWS Sagemaker, Google Cloud AI Platform, Azure ML) is a plus. Familiarity with software development best practices, including version control (Git), testing, and continuous integration/continuous deployment (CI/CD). Excellent analytical and problem-solving abilities with a strong desire to build practical, impactful solutions. * Ability to effectively communicate complex technical concepts to both technical and non-technical audiences.