AI Model Operations Specialist

1 week ago

Toronto, Ontario, Canada Canadian Tire Corporation Full time
About the Role

We are seeking a highly skilled AI Model Operations Specialist to join our AI and Emerging Technology group within Canadian Tire Corporation. As a key member of our team, you will be responsible for managing and optimizing our current MLOps Framework, and helping to implement, manage, and optimize a new LLMOps Framework to enable AI models throughout their lifecycle at CTC.

Key Responsibilities
  • Collaborate with cross-functional teams to deploy and manage AI models in production environments, ensuring stability, reliability, and performance.
  • Monitor the performance and health of AI models, proactively identifying and resolving issues to ensure optimal functionality.
  • Develop and implement robust model monitoring systems, alerting mechanisms, and automated processes for continuous evaluation.
  • Streamline the model deployment process in collaboration with data scientists, data engineers, cloud engineers, and software developers.
  • Contribute to the development and expansion of GenAI use cases, including standard processes, framework, templates, libraries, and best practices around GenAI.
  • Contribute to the development and implementation of best practices for model versioning, testing, documentation, and governance.
  • Ensure the availability, scalability, and security of AI model infrastructure by collaborating with IT teams.
  • Design, implement, and oversee the infrastructure required for the efficient operation of ML and LLMs.
  • Contribute to post-deployment analysis of AI models to identify areas for improvement and implement necessary enhancements.
  • Stay up to date with the latest advancements in AI technology, frameworks, and tools to make recommendations for adoption.
Requirements
  • Proven experience in deploying and managing AI models in production environments.
  • Experience with Azure services (e.g., ADF, ADLS, Databricks, AKV, etc.) and MLOps tools such as MLflow.
  • Strong understanding of machine learning concepts, LLMs, algorithms, and techniques.
  • Proficiency in programming languages commonly used in AI development such as Python, R, or TensorFlow.
  • Familiarity with LLM models and associated techniques, as well as the tools and frameworks used to implement them (LangChain, LangSmith, Vector store).
  • Experience with model deployment frameworks (e.g., TensorFlow Serving), containerization (e.g., Kubernetes, Docker), and cloud platforms (e.g., Azure, GCP).
  • Familiarity with DevOps practices and tools for CI/CD pipelines (e.g., GitLab, Jenkins) in the context of deploying AI models.
  • Proven experience in ML/LLM monitoring and observability, preferably using New Relic.
  • Adaptability and willingness to learn new things and stay on top of the GenAI trends.
  • Solid understanding of model performance metrics and evaluation techniques.
  • Excellent problem-solving skills with a focus on root cause analysis and continuous improvement.
  • Strong communication skills (both verbal and written) to effectively collaborate with cross-functional teams.
  • Ability to lead technical presentations, demonstrations, workshops, design sessions, proofs of concept, and pilots.
  • Ability to convey complex ideas to both technical and non-technical stakeholders.
  • Strong relationship-building and influencing skills.
  • Ability to work independently as well as part of a team in a fast-paced environment.
  • Practical knowledge of Infrastructure as Code (i.e. Terraform).
  • Bachelor's degree in Computer Science or a related technical discipline or equivalent combination of education, technical certifications/training, or work experience.
  • Knowledge of distributed computing frameworks (e.g., Apache Spark) for large-scale data processing.
  • Familiarity with data privacy regulations and ethical considerations in AI model development.
  • Experience with deep learning frameworks (e.g., PyTorch) and computer vision techniques.
About Our Company

We value flexibility and have adopted a hybrid work model whereby employees use a combination of working in office and virtually in service of outcomes. Each leader is empowered to decide what work is best achieved in person based on the unique needs of their team.

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