ML Ops SRE — Cloud & Kubernetes (Remote)

Pyramid, Inc; Austin (TX); À distance; USD 66 000 - 69 000; Plein temps

Résumé du poste

Pyramid Consulting, Inc. is seeking an SRE with an ML focus for a 06+ months contract located in Austin, TX / Sunnyvale, CA (Remote). The role centers on building robust ML ops platforms, cloud-based data pipelines, and scalable CI/CD for ML workloads.

Ideal candidates bring 6+ years in ML Ops, strong Kubernetes and Python skills, AWS experience, and familiarity with MLOps frameworks like Kubeflow and Airflow. Remote work options are available across locations.

Qualifications

• 6+ years of ML Ops experience with strong Kubernetes, Python, MongoDB and AWS
• Good understanding of Apache Solr
• Proficiency in Linux administration
• Experience with ML models and LLMs
• Ability to design and implement cloud-based MLOps pipelines
• Experience with MLOps frameworks like Kubeflow, MLFlow, Airflow or similar

Responsabilités

• Design and implement cloud solutions and ML pipelines on cloud platforms (AWS or GCP)
• Build MLOps CI/CD pipelines and orchestration (GitLab CI, GitHub Actions, Flux, Kustomize, Circle CI, Airflow)
• Containerize data science models and deploy with Docker, Kubernetes
• Review models, refactor code, ensure deployment, versioning and monitoring quality
• Automate tests for data science models and workflows
• Collaborate with data scientists, data engineers and architects to document processes
• Develop scalable tools and services for ML training and inference

Connaissances

ML Ops

Kubernetes

Python

AWS

Linux

LLM

API integrations

Outils

Docker

Kubeflow

MLFlow

Airflow

DataRobot

Description du poste

Pyramid Consulting, Inc. is seeking an SRE with an ML focus for a 06+ months contract located in Austin, TX / Sunnyvale, CA (Remote). The role centers on building robust ML ops platforms, cloud-based data pipelines, and scalable CI/CD for ML workloads.

Ideal candidates bring 6+ years in ML Ops, strong Kubernetes and Python skills, AWS experience, and familiarity with MLOps frameworks like Kubeflow and Airflow. Remote work options are available across locations.