RandomTrees
MLOPS Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an MLOps Engineer with a contract length of "unknown," offering a pay rate of "unknown." It requires remote work, expertise in MLOps, strong Python skills, GCP experience, and familiarity with CI/CD tools and ML frameworks.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
640
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🗓️ - Date
August 4, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Santa Monica, CA
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🧠 - Skills detailed
#Python #GCP (Google Cloud Platform) #Data Pipeline #Deployment #Scripting #Terraform #Monitoring #Dataflow #Bash #Infrastructure as Code (IaC) #TensorFlow #BigQuery #ML (Machine Learning) #Cloud #DevOps #Automation #SQL (Structured Query Language) #PyTorch
Role description
Job Title: MLOPS engineer.
Location: Remote (travel to Santa Monica, CA if required)
Technical Skills
Experience in MLOps, DevOps, or ML Engineering, with hands-on GCP experience
Strong Python skills; comfortable with Bash and scripting for automation
Proficiency with BigQuery — writing/optimizing SQL, scheduled queries, and integrating with ML pipelines
Experience with GKE or Cloud Run for containerized workloads
Experience with Cloud Build or similar CI/CD tools for automated deployment
Familiarity with Terraform or Deployment Manager for infrastructure as code
Understanding of GCS, Pub/Sub, and Dataflow for data pipeline construction
Experience with ML frameworks (TensorFlow, PyTorch, scikit-learn)
Solid understanding of the ML lifecycle: data versioning, training, validation, deployment, monitoring
Job Title: MLOPS engineer.
Location: Remote (travel to Santa Monica, CA if required)
Technical Skills
Experience in MLOps, DevOps, or ML Engineering, with hands-on GCP experience
Strong Python skills; comfortable with Bash and scripting for automation
Proficiency with BigQuery — writing/optimizing SQL, scheduled queries, and integrating with ML pipelines
Experience with GKE or Cloud Run for containerized workloads
Experience with Cloud Build or similar CI/CD tools for automated deployment
Familiarity with Terraform or Deployment Manager for infrastructure as code
Understanding of GCS, Pub/Sub, and Dataflow for data pipeline construction
Experience with ML frameworks (TensorFlow, PyTorch, scikit-learn)
Solid understanding of the ML lifecycle: data versioning, training, validation, deployment, monitoring
