Now100
Overview
Title:
Site Reliability Engineer SRE ML platform Location:
Austin, TX OR Sunnyvale, CA - Onsite Duration:
Long Term Contract Work Authorization:
USC/GC - W2 only Note:
60% SRE and 40% ML Ops Responsibilities
Continuous Deployment using GitHub Actions, Flux, Kustomize Design and implement cloud solutions, build MLOps on cloud AWS 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 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 Seniority level: Mid-Senior level
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Title:
Site Reliability Engineer SRE ML platform Location:
Austin, TX OR Sunnyvale, CA - Onsite Duration:
Long Term Contract Work Authorization:
USC/GC - W2 only Note:
60% SRE and 40% ML Ops Responsibilities
Continuous Deployment using GitHub Actions, Flux, Kustomize Design and implement cloud solutions, build MLOps on cloud AWS 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 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 Seniority level: Mid-Senior level
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