ML Ops Senior Engineer

Overview

On Site
Depends on Experience
Contract - W2
Contract - Independent
Contract - 6 Month(s)

Skills

ML Ops

Job Details

Must have:
Expertise in Python and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn, etc.).
Strong Experience in deployment/devops technologies: CI/CD pipelines, Kubernetes/Docker, and infrastructure-as-code tools (Terraform, Ansible, etc.). and cloud-native architectures (Google Cloud Platform and Aruze), monitoring and observability for ML workloads
Advanced understanding of ML pipeline orchestration tools like Kubeflow, MLflow, Airflow, or TFX.

Nice to have:
Experience with distributed computing frameworks (e.g., Spark, Ray, Dask) is a plus.
Familiarity with model explainability, fairness, and bias detection tools is highly desirable.
Strong knowledge of security best practices for ML systems, including data encryption, API security, and governance.

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