Machine Learning Engineer/ LLMOps Engineer-NO C2C
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer/LLMOps Engineer, remote, long-term contract. Key skills include Kubernetes, Docker, CI/CD, Python, and cloud services (AWS/Azure/GCP). Experience with LLM-specific frameworks and performance optimization techniques is required.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
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🗓️ - Date discovered
May 21, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Remote
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📄 - Contract type
Corp-to-Corp (C2C)
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🔒 - Security clearance
Unknown
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📍 - Location detailed
San Francisco Bay Area
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🧠 - Skills detailed
#Ansible #Cloud #MLflow #Python #Automation #ML (Machine Learning) #GCP (Google Cloud Platform) #Kubernetes #AWS (Amazon Web Services) #Terraform #DevOps #Bash #Docker #Azure
Role description
MLOps/LLMOps Engineer
Location: Remote
Duration: Long Term
Job Description::
•
• Key Responsibilities:
• DevOps + ML: Expertise in Kubernetes, Docker, CI/CD tools, and MLflow or similar platforms
• Cloud & Infrastructure: Understanding of GPU instance options, cloud services (AWS/Azure/GCP), and optimization techniques
• Automation: Proficiency in Python, Bash, and infrastructure-as-code tools like Terraform or Ansible
• LLM-Specific Frameworks: Experience with tools like TensorBoard, MLFLow, or equivalent for scaling LLMs
• Performance Optimization: Knowledge of techniques to monitor and improve inference speed, throughput, and cost
• Collaboration: Ability to work effectively across technical teams while adhering to enterprise architecture standards
MLOps/LLMOps Engineer
Location: Remote
Duration: Long Term
Job Description::
•
• Key Responsibilities:
• DevOps + ML: Expertise in Kubernetes, Docker, CI/CD tools, and MLflow or similar platforms
• Cloud & Infrastructure: Understanding of GPU instance options, cloud services (AWS/Azure/GCP), and optimization techniques
• Automation: Proficiency in Python, Bash, and infrastructure-as-code tools like Terraform or Ansible
• LLM-Specific Frameworks: Experience with tools like TensorBoard, MLFLow, or equivalent for scaling LLMs
• Performance Optimization: Knowledge of techniques to monitor and improve inference speed, throughput, and cost
• Collaboration: Ability to work effectively across technical teams while adhering to enterprise architecture standards
