MLOps Engineer

UPDATE

While this role has to do with MLOps hes not as concerned about a candidate being an expert so as long as they are very senior with Advanced Terraform and Advanced AWS for DevOps and that they can figure out the MLOps part. So he will select the candidate who is the strongest in Terraform and AWS for DevOps. Said if candidates arent well versed here then he doesnt even want to even review them and he cannot stress this enough. Doesnt want any junior candidates have be at least 8 years.

Additionally they need a basic knowledge of networking with be using GIT hub cloud to integrate with AWS.

Team size is about 5 and will work with data scientists.

This contract should be extended out past 6 months (and could keep going) for the right person.

Hes fine with virtual interviews should just be 1 but maybe 2.

So top skills are Advanced Terraform Advanced AWS for DevOps and MLOps being more secondary compared to those first 2.

Job Title - MLOps Engineer III

Job Location - Downtown Houston Texas 77002

Job Description -

We are seeking a skilled MLOps/DevOps Engineer to join our team as a contractor.

In this role youll be instrumental in developing maintaining and scaling our cloud infrastructure on Amazon Web Services (AWS).

Your expertise will be key in enabling our machine learning teams to deploy manage and monitor their models efficiently all while building a robust and automated environment under minimal supervision.

Top daily responsibilities:

Building out terraform automations for AWS.
Providing customer service for Data Scientists and AI Engineers.
MLOps & DevOps for GitHub and AWS.

Other responsibilities include:

Infrastructure Management: Work with the platform/infrastructure teams to implement to improve and to automate infrastructure as code (IaC) to ensure consistency and repeatability.
CI/CD Pipeline Development: Build and maintain robust CI/CD pipelines for both application and machine learning model deployments. This includes automating the entire workflow from code commit to production deployment.
MLOps Implementation: Collaborate with data scientists and machine learning engineers to create and optimize the MLOps lifecycle. This involves automating model training versioning serving and monitoring in a production environment.
Automation: Automate repetitive tasks using scripts and tools to streamline operations and improve efficiency.
Security: Working closely with the security teams ensure the security of our cloud environment by implementing best practices managing access controls and conducting regular security audits.
Collaboration: Work closely with development data science and product teams to understand their needs and provide technical solutions that meet business goals.

The successful candidate will meet the following qualifications:

Degree from an accredited college in computer science or related industry experience is required.
8 years of professional DevOps experience is required.
2 years of professional MLOps experience is required.
5 years of strong expertise in the AWS ecosystem (e.g. EC2 S3 RDS Lambda SageMaker EKS) is required.
Hands-on experience with Terraform for building and managing infrastructure as code (IaC) is required.
Solid understanding of MLOps principles and the challenges of deploying machine learning models at scale is required.
8 years of professional experience building and managing CI/CD pipelines is required.
Experience with containerization technologies (Docker Kubernetes) is required.
Strong communication skills and ability to work effectively in a team environment is required.
5 years of professional experience with GitHub or similar code repositories is required.
Strong knowledge of DevOps automations in the AWS ecosystem is required.
Knowledge of scripting YAML JSON Shell Bash and Python is required.
Hands-on experience with building and managing security as code (SaC) is preferred.
Understanding basic networking principles between AWS and on-premises networks is preferred.