AI/ML Engineer

Triunity Software•Dc, WA

About The Position

Triunity Software is seeking a talented and driven AI/ML Engineer to join its team in Calgary, Alberta. The role involves building innovative artificial intelligence and machine learning solutions, turning data into intelligent systems, and working with cross-functional teams to address business problems. The engineer will develop and deploy machine learning models and AI applications, maintain scalable data and model-training infrastructure, and monitor and improve production systems. Triunity is a product development, staff augmentation, and consulting services company serving clients across North America and business sectors including healthcare, pharmaceuticals, banking, and finance. The company describes a team-oriented culture focused on challenging projects, new technologies, collaboration with industry experts, and employee learning and career growth.

Requirements

Bachelor's or master's degree in Computer Science, Data Science, Mathematics, or a related field.
Proven experience building and deploying machine learning or deep learning models in production.
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 tools.
Strong understanding of data preprocessing, feature engineering, and model evaluation techniques.
Familiarity with SQL/NoSQL databases and big data technologies.
Excellent problem-solving skills and ability to communicate technical concepts clearly.

Responsibilities

Design, develop, and deploy machine learning models and AI-driven applications.
Collaborate with cross-functional teams to identify and solve complex business problems using machine learning solutions.
Build and maintain scalable data pipelines and model-training infrastructure.
Evaluate models, tune performance, and continuously improve deployed systems.
Research and implement state-of-the-art algorithms in natural language processing, computer vision, or predictive analytics.
Monitor production machine learning models and ensure their reliability, accuracy, and scalability.
Document technical processes, model architectures, and experiment results.