2 months ago
Remote, CanadaSenior
Responsibilities
- Design enterprise MLOps and LLMOps architectures.
- Build deployment strategies for AI and LLM solutions.
- Define model lifecycle management processes.
- Implement monitoring, observability, and evaluation frameworks.
- Design CI/CD pipelines for machine learning workloads.
- Collaborate with AI researchers, platform engineers, and DevOps teams.
- Support AI governance and security requirements.
- Advise client stakeholders on operational AI best practices.
Requirements
- 7+ years of experience in Machine Learning Engineering or MLOps.
- 3+ years designing production-grade MLOps platforms.
- Experience deploying and operationalizing LLMs and GenAI applications in enterprise environments.
- Strong knowledge of MLflow, Kubeflow, Vertex AI, Azure ML, SageMaker, or similar platforms.
- Knowledge of RAG architectures, vector databases, model evaluation, and prompt management.
- Experience with Kubernetes, Docker, and CI/CD pipelines.
- Familiarity with GPU infrastructure.
- Strong understanding of AI governance and model lifecycle management.
- Upper-Intermediate or higher level of English.
Benefits
- The role is based in Canada, with Alberta-based candidates in Calgary or Edmonton strongly preferred; other Canada-based candidates will also be considered.
Tech Stack
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