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ML Ops Engineer

Hybrid in San Francisco, CA, US • Posted 1 day ago • Updated 1 day ago

Contract W2

Contract Independent

No Travel Required

Hybrid

$70 - $75/hr

Fitment

Dice Job Match Score™

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Job Details

Skills

MLOps
AI/ML
Python
Cloud

Summary

Key Responsibilities

Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
Automate model training, testing, deployment, and monitoring in cloud environments (e.g., Google Cloud Platform, AWS, Azure).
Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment

Qualifications

10+ Years of professional experience in Software Engineering & 3+ Years in AIML, Machine Learning Model Operations.
Strong proficiency in Java and Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
Experience with cloud platforms and containerization (Docker, Kubernetes).
Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
Solid understanding of software engineering principles and DevOps practices.
Ability to communicate complex technical concepts to non-technical stakeholders.

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91132369
Position Id: 8958104
Posted 1 day ago

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