MLOPS - Machine Learning Operations Engineer

Targeted Years of Experience: 3-5 years
The MLOPS engineer will be responsible for developing and managing the continuous integration and delivery pipeline for machine learning models. They will work with data scientists and engineers to automate the process of training, testing, and deploying models. They will also monitor the performance of the models in production and troubleshoot issues as needed. In this role, you will have the opportunity to work with some of the latest technologies and tools to build scalable and reliable systems. If you are passionate about DevOps and machine learning, this is the role for you!
Creating, deploying, and monitoring AI/ML pipelines
Create and maintain the MLOps production infrastructure and services.
Drive software development methods such as code profiling, regression testing, continuous integration, and push button deployments.
Develop/maintain processes, tools, and documentation to support production.
Ensure adequate infrastructure security.

Assist in the evaluation of new software, hardware, and infrastructure solutions.
Experience with a variety of scripting languages for automating tasks, generating reports, and creating tools (e.g. Python, Shell, SQL)

MUST HAVE SKILLS (Most Important):
5+ years of experience in CI/CD, ML Pipelines, and Python
3+ years of experience in Big Data, such as Teradata and Bigquery

DESIRED SKILLS:
Experience with a public cloud provider, such as AWS, Azure, or GCP
Experience with containerization, such as Docker or Kubernetes

EDUCATION/CERTIFICATIONS:
B.S. in Computer Science or related field.

Originally posted on Himalayas