Senior ML Ops Engineer: Cloud ML Pipelines & CI/CD

Compunnel, Inc.

California (MO)

On-site

USD 120,000 - 160,000

Full time

14 days+
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Job summary

A leading technology firm is looking for an experienced ML Ops Senior Engineer to support the lifecycle of machine learning solutions. Responsibilities include building ML pipelines, automating model deployment, and ensuring model governance. Ideal candidates have over 10 years in software engineering, solid skills in Java and Python, and expertise in cloud platforms like AWS, GCP, or Azure. This role requires collaboration with cross-functional teams to deliver effective ML solutions.

Qualifications

  • 10+ years of professional software engineering experience.
  • 3+ years of hands-on experience in AI/ML engineering or ML Ops.
  • Strong proficiency in Java, Python, SQL, and ML libraries.
  • Deep experience with cloud platforms and containerization technologies.
  • Solid understanding of CI/CD, DevOps, and automation.

Responsibilities

  • Develop and maintain end-to-end ML pipelines using MLflow, Kubeflow, or Vertex AI.
  • Automate model training, testing, deployment, monitoring, and retraining.
  • Implement CI/CD workflows for ML lifecycle management.
  • Monitor production model performance using observability tools.
  • Collaborate with engineering teams for containerized environments.

Skills

Software engineering
Machine learning operationalization
CI/CD automation
Cloud native ML platform engineering
Java
Python
SQL
ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch)
Docker
Kubernetes
Airflow
Spark

Tools

MLflow
Kubeflow
Vertex AI

Job description

A leading technology firm is looking for an experienced ML Ops Senior Engineer to support the lifecycle of machine learning solutions. Responsibilities include building ML pipelines, automating model deployment, and ensuring model governance. Ideal candidates have over 10 years in software engineering, solid skills in Java and Python, and expertise in cloud platforms like AWS, GCP, or Azure. This role requires collaboration with cross-functional teams to deliver effective ML solutions.
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