Site Reliability Engineer with ML platform - Only W2

Saransh Inc; Sunnyvale (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

A tech company is seeking a Site Reliability Engineer (SRE) for their machine learning platform. The role involves designing and implementing cloud solutions, developing tools for machine learning, and continuous deployment. The ideal candidate has over 6 years of experience in ML Ops and is proficient in Kubernetes, AWS, and Python. This is a full-time position based in Sunnyvale, CA.

Qualifications

• 6+ years of experience in ML Ops and strong knowledge in Kubernetes, Python, MongoDB, and AWS.
• Good understanding of Apache SOLR.
• Ability to design and implement cloud solutions and build MLOps pipelines.

Responsibilities

• Continuous Deployment using GitHub Actions, Flux, Kustomize.
• Design and implement cloud solutions for ML.
• Develop and deploy scalable tools for machine learning.

Skills

Kubernetes

Python

MongoDB

AWS

Linux administration

ML models and LLM

Tools

Docker

Kubeflow

MLFlow

Airflow

Job description

Overview

Title: Site Reliability Engineer SRE – ML platform

Location: Austin, TX or Sunnyvale, CA

Employment type: Full-time • Seniority: Mid-Senior level • ONLY W2

Responsibilities

• Continuous Deployment using GitHub Actions, Flux, Kustomize
• Design and implement cloud solutions, build MLOps on cloud AWS
• Data science model containerization, deployment using docker, VLLM, Kubernetes
• 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
• Knowledge of ML models and LLM

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)
• 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