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Site Reliability engineer MLops

Delviom LLCSunnyvale, CA🇺🇸United StatesPosted 6 Aug 2026

Quick Overview

Work Type
Hybrid
Level
Mid Senior

Job Description

Role: SRE – ML focus

Location - Sunnyvale, CA/Austin, TX

Responsibilities –

  • Design and implement cloud solutions, build MLOps on cloud (AWS or Google Cloud Platform)
  • Build CI/CD pipelines orchestration by GitLab CI, GitHub Actions, Flux, Kustomize, Circle CI, Airflow or similar tools
  • Data science model containerization, deployment using docker, VLLM, Kubernetes
  • Data science model review, run the code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality
  • Data science models testing, validation and tests automation
  • Communicate with a team of data scientists, data engineers and architects, document the processes
  • Develop and deploy scalable tools and services for our clients to handle machine learning training and inference

Qualifications:

  • 6+ years of experience in ML Ops with strong knowledge in Kubernetes, Python, MongoDB and AWS.
  • Good understanding of Apache SOLR.
  • Proficient with Linux administration.
  • Knowledge of ML models and LLM.
  • Ability to understand tools used by data scientists and experience with software development and test automation
  • Ability to design and implement cloud solutions and ability to build MLOps pipelines on cloud solutions (AWS or Google Cloud Platform)
  • Experience working with cloud computing and database systems
  • Experience building custom integrations between cloud-based systems using APIs
  • Experience developing and maintaining ML systems built with open-source tools
  • Experience with MLOps Frameworks like Kubeflow, MLFlow, DataRobot, Airflow etc., experience with Docker and Kubernetes
  • Experience developing containers and Kubernetes in cloud computing environments
  • Familiarity with one or more data-oriented workflow orchestration frameworks (Kubeflow, Airflow, Argo, etc.)
  • Ability to translate business needs to technical requirements
  • Strong understanding of software testing, benchmarking, and continuous integration
  • Exposure to machine learning methodology and best practices
  • Good communication skills and ability to work in a team

Skills

Docker
MongoDB
AWS
MLOps
MLflow
Machine Learning
Airflow
Apache
GitHub Actions
GitLab CI
Google Cloud
Kubernetes
LLM
Python

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