Design and architect enterprise-grade MLOps and LLMOps platforms, build deployment strategies and CI/CD pipelines, define model lifecycle and governance, implement monitoring/observability and evaluation frameworks, collaborate with researchers and DevOps, and advise stakeholders on operationalizing secure GenAI solutions.
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.
ABOUT CLIENT
Our customer is building a next-generation AI platform that enables organizations to securely develop, govern, and operationalize artificial intelligence while ensuring that sensitive data and organizational knowledge remain fully under their control. The platform combines advanced AI capabilities with enterprise-grade governance, security, and data sovereignty to support mission-critical decision-making.
The solution serves government organizations and enterprise customers operating in highly regulated and security-sensitive environments, where reliability, accountability, and trust are essential. The platform supports intelligent decision-making across strategic planning, workforce intelligence, and organizational operations, helping customers leverage AI without compromising security, compliance, or control over their data.
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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