MLOps Consultant, Senior

7 days ago

Toronto, Ontario, Canada 1b0a8fec-2b66-4147-92a9-5a8a8407cdaf Full time $80,000 - $125,000 per year

Senior MLOps Consultant (Agentic AI Engineering Delivery)
Overview
Our client, a leading organization in the banking sector, is seeking a
Senior MLOps Consultant
with proven, hands-on experience building and deploying production-grade
Agentic AI
systems. This role is deeply technical, requiring strong
software engineering and platform delivery expertise
rather than QA or oversight. The ideal candidate has
built and shipped complex ML/AI systems
leveraging MLOps best practices and modern GenAI frameworks.

This consultant will be instrumental in designing, automating, and scaling ML and GenAI workloads within secure, enterprise-grade environments—combining MLOps engineering, Agentic AI development, and platform reliability into one cohesive delivery function.

Key Responsibilities

  • Design, implement, and optimize end-to-end MLOps pipelines for model training, testing, deployment, and monitoring.
  • Build, ship, and operationalize Agentic AI systems and large-scale ML workflows with a focus on automation, scalability, and reliability.
  • Develop and maintain robust platform engineering solutions to support high-performance ML/GenAI workloads.
  • Collaborate with cross-functional teams (Data Science, Engineering, Cloud Architecture) to deliver secure and high-quality systems.
  • Manage containerized AI workloads using Docker and Kubernetes for efficient orchestration and scaling.
  • Integrate ML pipelines into cloud and enterprise data ecosystems (AWS, Azure, or GCP).
  • Continuously improve system observability, model performance, and deployment automation.
  • Contribute to engineering best practices, reusable frameworks, and documentation to enable delivery excellence.

Required Skills & Experience

  • 7+ years of hands-on engineering experience across MLOps, DevOps, or Data Engineering with a strong focus on delivery and system build (not QA or management).
  • Proven track record building and shipping Agentic AI or ML systems in production environments.
  • Expert-level Python programming skills for production-grade system development.
  • Deep knowledge of ML lifecycle management, including model versioning, monitoring, and governance.
  • Strong expertise in Docker, Kubernetes, and related CI/CD tooling (GitLab CI, Jenkins, ArgoCD, etc.).
  • Experience implementing or extending GenAI frameworks (LangChain, Hugging Face, OpenAI APIs, etc.) for enterprise use cases.
  • Solid understanding of cloud-native ML platforms such as AWS SageMaker, GCP Vertex AI, or Azure ML.
  • Strong communication, analytical, and problem-solving abilities, with experience in financial services or secure enterprise environments preferred.

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