AI/ML Engineer

Triunity Software•San Francisco, CA

About The Position

Triunity Software is seeking a talented and driven AI/ML Engineer to join its team in Calgary, Alberta. The engineer will develop and deploy artificial intelligence and machine learning solutions, collaborate with cross-functional teams to identify opportunities, and help turn data into intelligent systems. The role includes building scalable data pipelines and model-training infrastructure, evaluating and improving model performance, researching current ML techniques, monitoring deployed models, and documenting technical processes and specifications. Triunity is a product development, staff augmentation, and consulting services company providing IT services and technology solutions to businesses across North America and sectors including healthcare, pharmaceuticals, banking, and finance. Its culture emphasizes teamwork, challenging projects, exposure to new technologies, and opportunities to learn and grow through training, seminars, and knowledge-sharing sessions.

Requirements

Bachelor's or master's degree in Computer Science, Data Science, or a related field.
Proven experience building and deploying machine learning or deep learning models.
Proficiency in Python and machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
Experience with cloud platforms such as AWS, Azure, or GCP and MLOps practices.
Strong understanding of data preprocessing, feature engineering, and model evaluation.
Excellent problem-solving skills and ability to work in an agile environment.

Nice To Haves

Familiarity with NLP, computer vision, or generative AI is a strong asset.

Responsibilities

Design, develop, and deploy machine learning models and AI-driven solutions.
Collaborate with cross-functional teams to identify and scope AI/ML opportunities.
Build and maintain scalable data pipelines and model-training infrastructure.
Evaluate and optimize model performance using relevant metrics and testing frameworks.
Research and implement state-of-the-art machine learning algorithms and techniques.
Monitor deployed models and iterate based on real-world performance data.
Document technical processes, architectures, and model specifications.