ML Ops Technical Specialist - DevOps, Python

About the role

This role is responsible for architecting, designing, and delivering robust machine learning operations solutions that integrate DevOps practices with scalable ML pipelines. The individual drives technical excellence, ensures adoption of best practices, and enables high-quality, automated workflows for model deployment, monitoring, and lifecycle management.

What you'll do

• Architect and implement ML Ops solutions using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, deployment, and monitoring processes
• Design and manage CI/CD pipelines for ML projects utilizing Jenkins, GitLab CI/CD, CircleCI
• Develop infrastructure-as-code templates with Terraform and AWS CloudFormation to provision and manage scalable cloud environments for ML workloads
• Integrate monitoring and logging solutions using Prometheus, Grafana, ELK Stack, and Fluentd to enable real-time performance tracking and issue resolution for ML systems
• Lead the adoption of DevOps practices by configuring version control systems such as Git, GitHub, GitLab, and Bitbucket for collaborative development and reproducibility

What you need

• 1–3 years in data science & ai
• Python
• MLOps
• Machine learning
• AWS
• CI/CD
• Git
• Any graduate

Good to have

• Terraform
• Jenkins
• Prometheus
• Grafana
• Elasticsearch
• GCP