The posting
Role Overview
The Artificial Intelligence Engineer at Precision AI will contribute to the design, development, and deployment of AI-driven solutions. This role focuses on building, training, and optimizing machine learning models, supporting AI projects from experimentation through production, and maintaining high standards of technical quality.
Working closely with Senior AI Engineers and cross-functional partners, the AI Engineer will help implement AI solutions that address real-world problems. This role offers the opportunity to grow technical expertise while collaborating within a fast-paced, research-driven environment.
This hybrid role is based in Calgary and will work from Precision AI’s headquarters 3 days a week.
Key Responsibilities
AI Project Leadership Plan, design, and oversee AI/ML projects from concept to deployment. Define milestones, monitor progress, and ensure timely delivery.
Model Development & Innovation Build, train, evaluate, and optimize machine learning models across natural language processing, computer vision, and multimodal domains, including LLMs, VLMs, and vision-specific models (e.g., CNNs, ViTs, diffusion-based models). Apply a range of techniques such as transfer learning, parameter-efficient fine-tuning, prompt engineering, knowledge distillation, multimodal fusion, and efficient inference methods (quantization, pruning, model compression). Work with recent large language models and reasoning-oriented models, applying techniques such as supervised fine-tuning, structured prompting, retrieval-augmented generation (RAG). Read and experiment with recent technologies and research papers; evaluate applicability to projects.
Programming Excellence Apply strong foundations in data structures, algorithms, object-oriented programming, and software design patterns to build reliable AI systems. Write clean, maintainable, and well-documented code following established team standards. Practice unit/integration testing, CI/CD pipelines, and version control (Git/GitHub). Leverage containerization and orchestration tools such as Docker and Kubernetes for reproducible development and deployment. Design and consume APIs (REST/GraphQL) for integrating AI models into larger systems.
Mentorship & Team Development Guide junior engineers through technical challenges and project progress. Promote knowledge sharing through code reviews, workshops, and documentation.
Cloud Infrastructure & Data Systems Design and manage scalable solutions on AWS, leveraging cloud-native tools and best practices. Work with large-scale datalake architectures to support data-driven applications. Assist in monitoring and maintaining deployed models and services.
Technical Communication and Partner Management Communicate technical progress, challenges, and results clearly within the team. Contribute to internal documentation and project updates
Relevant Experience
Required 4+ years of experience in AI/ML model design, training, and deployment in production environments. Proven expertise in building and optimizing models, including LLMs, VLMs, and other deep learning architectures. Exposure to transfer learning, self-supervised learning, multimodal AI systems and domain generalization. Knowledge of retrieval-augmented generation (RAG), diffusion models, or other cutting-edge ML techniques. Strong programming skills in Python with solid knowledge of data structures, algorithms, and software engineering best practices. Hands-on experience with large-scale datalake architectures and distributed data processing Experience with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and MLOps practices (CI/CD, experiment tracking, reproducibility). Strong communication, documentation, and presentation skills, with the ability to work across teams and with external partners. Ability to stay current with emerging AI research and assess applicability of new methods to real-world problems.
Education Requirements Bachelor's or master's degree in computer science, computer engineering, statistics, or mathematics
