Responsibilities

• Design, scale, and optimize computer vision pipelines to serve thousands of clients.
• Own the infrastructure that powers ML models in production.
• Automate workflows, improve observability, and ensure reliable, cost-efficient deployment at scale.

Qualifications

• 3–5+ years of experience in DevOps, MLOps, or ML engineering roles.
• Experience automating ML workflows using CI/CD pipelines (e.g., Github actions, GitLab CI, Jenkins, AWS CodePipeline).
• Strong coding skills in Python and experience writing unit and integration tests for ML pipelines.
• Hands-on experience with containerization (Docker) and AWS infrastructure (ECS, S3, AWS Batch).
• Familiarity with infrastructure-as-code (e.g., Terraform or CloudFormation).
• Experience building observability systems for ML (metrics, logs, model performance tracking).
• Understanding of model versioning, reproducibility, and testing best practices.
• Awareness of cloud cost optimization practices (e.g., spot instances, autoscaling).
• Experience supporting a production ML environment and collaborating across cross-functional teams.

Nice to Have

• Experience with ML lifecycle tools (e.g., Weights & Biases, Voxel 51).
• Familiarity with data validation frameworks (e.g., Great Expectations).
• Exposure to real-time monitoring tools (e.g., Prometheus, Grafana, OpenTelemetry).

Protex AI is an inclusive and equal opportunities employer. We are committed to creating an equitable workplace for everyone regardless of gender, civil status, family status, sexual orientation, religion, age, disability, education level, or race.