MLOps Engineer (AI)

Application deadline date has been passed for this Job.
Full Time
  • Post Date: 03/12/2021
Job Description

Job Description

Our client is a is an Information Technology & Services company offering products and services such as telecom, AI, customer relationship management platform and more.  They are currently looking for an MLOps Engineer as a lead role for our DevOps environment for AI team with his experiences, best practices, and a collaborative attitude to help drive DevOps initiatives. The responsibilities include both managing and building processes for automation as well as contributing to the development of internal tools to achieve operational efficiency.

Job Responsibilities

  • Maintain AI infrastructure clusters
  • Maintain models training infrastructure (GPU clusters)
  • Deploy and maintain Kubeflow infrastructure
  • Design and implement alerts system for models quality and AI services availability
  • Deploy and maintain hyper-parameters tuning infrastructure
  • Prioritizing requests from AI team fairly while demonstrating a sense of empathy
  • Maintain and enhance our CI/CD pipelines for AI
  • Collaborate with data engineering team to support production grade AI system
  • Develop automation flows that enable fast delivery and replace manual operating procedures wherever they exist to enable self-service operations
  • Drive analysis, design, and development of automation tools for deployment, development, and operational tasks
  • Deploy & manage monitoring/observability infrastructure for staging & production
  • Collaborate with DevOps team to enhance common infrastructure
  • Make sure new environments meet requirements and conform to best practices


  • 2+ years’ experience within hands-on technical DevOps/Cloud engineering
  • Good knowledge of Python or Golang
  • Experience with Kubernetes deployment patterns and tools such as Helm, Kustomize and Operators
  • Experience utilizing DevOps tool chains including Jenkins, Docker, SonarQube, GitHub
  • Experience with tools used for observability such as Elasticsearch, Kibana, Grafana, Prometheus, Jaeger etc.
  • Experience with SQL & NoSQL databases such as PostgreSQL and MongoDB
  • Experience with event steaming tools (i.e. Apache Kafka) and architecture patterns
  • Exposure to Agile environments (use of Jira/Confluence, sprints, etc.)
  • Good understanding of Machine Learning project life-cycle
  • Great communication skills and team player mentality


  • Experience with production grade machine learning systems
  • Advanced knowledge of Fairing frameworks or Kubeflow
  • Experience with development of custom Kubernetes operators
  • Experience with AutoML infrastructure
  • Infrastructure as Code experience (Terraform, CloudFormation, etc.)
  • Experience with Azure public clouds is a plus
  • Understanding of network engineering and security principles (e.g. protocols, routing, switching, filtering, firewall rules, etc.)