MLOps Engineer
MLOps Engineer
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1 Vacancy
Job Description
Position Summary
We are seeking an experienced and highly motivated MLOps Engineer to join our dynamic Data and AI team. In this role youll bridge the gap between machine learning development and scalable production systems. You will be responsible for building automating and managing end-to-end ML pipelines that enable reliable and repeatable deployment of models across environments.
Key ResponsibilitiesDevelop and Implement CI/CD Pipelines: Design build and maintain scalable ML infrastructure and pipelines (CI/CD) for training testing deploying and monitoring models.
Automation and Orchestration: Automate model versioning deployment and rollback strategies across staging and production.
Collaboration: Collaborate closely with Data Scientists and Machine Learning Engineers to productionize ML models.
Deploy Infrastructure Operations: Apply Infrastructure as Code (IaC) to provision and manage ML infrastructure in the cloud.
Monitoring and Troubleshooting: Implement observability for ML systems including monitoring logging and alerting of model drift and data anomalies. Optimize performance and scalability of model training and inference systems.
Security and Compliance: Ensure security compliance and reliability of ML operations across cloud platforms.
Documentation: Maintain comprehensive documentation of systems processes and workflows to facilitate knowledge sharing and collaboration.
Desired Skills and ExperienceRequirements:
Education: Bachelors Degree in Computer Science Engineering or a related field.
Experience: 5 years of experience in MLOps DevOps or ML Engineering.
Azure DevOps and AzureML experience.
Technical Expertise:
Proficiency in cloud platforms (AWS Azure GCP) and containerization technologies (Docker Kubernetes).
Strong proficiency in Python Bash Powershell and experience with REST APIs
Experience with infrastructure as code (Terraform ARM).
Tool Proficiency:
Familiarity with CI/CD tools (Jenkins GitHub Actions ADO Pipelines)
Hands-on experience with ML frameworks: TensorFlow PyTorch Scikit-learn
Familiarity with ML tools like MLflow TFX DVC or Kubeflow
Experience with workflow orchestration (e.g. Apache Airflow Prefect)
Preferred Experiences:
Advanced Analytics Tools: Experience with advanced analytics tools and methodologies including monitoring and logging solutions (Azure Monitor Prometheus Grafana).
Agile Methodologies: Experience working in Agile development environments.
Communication: Strong verbal and written communication skills capable of articulating complex technical concepts to both technical and non-technical stakeholders.
Team Collaboration: A collaborative mindset with a track record of working effectively within diverse teams.
Other Qualifications:
AZ-400 DevOps Engineer Certification or Certified Kubernetes Administrator (CKA) is desired.
Experience with Data Science and Machine Learning teams is desired.
Employment Type
Full-time
