Site Reliability engineer MLops

Delviom LLC

Austin, TX, United States

6 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

6 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Testing (Software) Application Programming Interfaces (APIs) Airflow Amazon Web Services Automation of Tests Cloud Computing Databases Continuous Integration Github Python (Programming Language) Linux System Administration Machine Learning

+18 more

MongoDB Open Source Technology Cloud Services DataOps Software Engineering Apache Solr Management of Software Versions Circleci Google Cloud Large Language Models Containerization Gitlab-ci Kustomize Configuration Management Kubernetes Enterprise Integration Machine Learning Operations Code Restructuring Docker

Job description

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

Requirements

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