TekTRnd
Principal ML Ops Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Principal ML Ops Engineer with a long-term contract in San Francisco, CA (remote). Requires 5+ years in ML Ops, expertise in Git, Terraform, Kubernetes, and Python, plus security clearance.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
544
-
🗓️ - Date
August 2, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Remote
-
📄 - Contract
Unknown
-
🔒 - Security
Yes
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📍 - Location detailed
United States
-
🧠 - Skills detailed
#Programming #Python #GitHub #GIT #ML (Machine Learning) #Deep Learning #Automation #Cloud #Kubernetes #Monitoring #Jenkins #GCP (Google Cloud Platform) #ML Ops (Machine Learning Operations) #Deployment #Strategy #Terraform #Azure #Scala #Automated Testing #AI (Artificial Intelligence) #Computer Science #DevOps #Agile #AWS (Amazon Web Services) #Security #Ansible #Cybersecurity
Role description
Required US Citizens With Security Clearance Only
Role: Principal ML Ops Engineer, AI Inference
Location: San Francisco, CA ( Remote )
Duration: Long Term Contract
We are seeking an experienced ML Ops engineer to lead the architecture and implementation of scalable, production-grade AI inference solutions. You will work closely with our product and research teams to scale SOTA deep learning products and software, focusing on building and releasing high-performance AI runtimes.
Responsibilities
Architect and manage scalable model training and deployment pipelines for enterprise clients.
Lead the strategy for managing and releasing upstream and midstream AI product builds.
Design and implement automated testing frameworks to ensure model correctness, responsiveness, and efficiency.
Troubleshoot, debug, and upgrade mission-critical Dev & Test pipelines.
Define and deploy cybersecurity measures, including continuous vulnerability assessment and risk management for AI systems.
Collaborate with cross-functional teams to define market requirements and establish best practices for LLMOps.
Stay at the forefront of AI technologies and standards, driving innovation within our practice.
Qualifications
5+ years of experience in ML Ops, DevOps, and Automation, with a focus on enterprise software deployment.
Expertise with Git, Github Actions, Terraform, Jenkins, Ansible, and modern automation/monitoring technologies.
Extensive experience administering Kubernetes/OpenShift in production environments.
Deep understanding of Agile development methodologies.
Proven experience with at least one major cloud provider: AWS, GCP, Azure, or IBM Cloud.
Expert-level Python programming skills.
Advanced troubleshooting and systems-thinking skills.
Experience contributing to open-source AI/ML projects (e.g., vLLM) is a strong plus.
Bachelor’s degree or higher in Computer Science or a related discipline is preferred, but we prioritize practical experience and technical excellence.
Skills: agile,jenkins,ops,terraform,gcp,azure,ibm,git,ml,aws,python,cloud
Required US Citizens With Security Clearance Only
Role: Principal ML Ops Engineer, AI Inference
Location: San Francisco, CA ( Remote )
Duration: Long Term Contract
We are seeking an experienced ML Ops engineer to lead the architecture and implementation of scalable, production-grade AI inference solutions. You will work closely with our product and research teams to scale SOTA deep learning products and software, focusing on building and releasing high-performance AI runtimes.
Responsibilities
Architect and manage scalable model training and deployment pipelines for enterprise clients.
Lead the strategy for managing and releasing upstream and midstream AI product builds.
Design and implement automated testing frameworks to ensure model correctness, responsiveness, and efficiency.
Troubleshoot, debug, and upgrade mission-critical Dev & Test pipelines.
Define and deploy cybersecurity measures, including continuous vulnerability assessment and risk management for AI systems.
Collaborate with cross-functional teams to define market requirements and establish best practices for LLMOps.
Stay at the forefront of AI technologies and standards, driving innovation within our practice.
Qualifications
5+ years of experience in ML Ops, DevOps, and Automation, with a focus on enterprise software deployment.
Expertise with Git, Github Actions, Terraform, Jenkins, Ansible, and modern automation/monitoring technologies.
Extensive experience administering Kubernetes/OpenShift in production environments.
Deep understanding of Agile development methodologies.
Proven experience with at least one major cloud provider: AWS, GCP, Azure, or IBM Cloud.
Expert-level Python programming skills.
Advanced troubleshooting and systems-thinking skills.
Experience contributing to open-source AI/ML projects (e.g., vLLM) is a strong plus.
Bachelor’s degree or higher in Computer Science or a related discipline is preferred, but we prioritize practical experience and technical excellence.
Skills: agile,jenkins,ops,terraform,gcp,azure,ibm,git,ml,aws,python,cloud
