MLOps/LLMOps Architect

Eleks

$120K — $150K *
US-AnywhereRemote in Canada
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 7+ years of experience in Machine Learning Engineering or MLOps
  • 3+ years designing production-grade MLOps platforms
  • Experience with LLM deployment and operationalization
  • Strong knowledge of MLflow, Kubeflow, Vertex AI, Azure ML, SageMaker, or similar platforms
  • Experience deploying GenAI applications in enterprise environments
  • 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

Responsibilities

  • Design enterprise MLOps and LLMOps architecture
  • 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

Benefits

  • Flexible working environment
  • Professional development opportunities
  • Access to cutting-edge AI technologies
  • Collaborative team culture
  • Health and wellness programs
  • Support for work-life balance
Full Job Description
ELEKS is looking for a MLOps/LLMOps Architect in Canada.

Alberta-based candidates are strongly preferred (Calgary or Edmonton). Canada-based candidates will also be considered.

REQUIREMENTS

  • 7+ years of experience in Machine Learning Engineering or MLOps
  • 3+ years designing production-grade MLOps platforms
  • Experience with LLM deployment and operationalization
  • Strong knowledge of MLflow, Kubeflow, Vertex AI, Azure ML, SageMaker or similar platforms
  • Experience deploying GenAI applications in enterprise environments
  • Knowledge of RAG architectures, vector databases, model evaluation, and prompt management
  • Experience with Kubernetes, Docker, CI/CD pipelines
  • Familiarity with GPU infrastructure
  • Strong understanding of AI governance and model lifecycle management
  • Upper-Intermediate or higher level of English


RESPONSIBILITIES

  • Design enterprise MLOps and LLMOps architecture
  • 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


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