Tile: ML-Ops Engineer (Machine Learning Operations)
Contract Type: 3 monthContract to hire
Conversion Salary: 90-95K
Location: Hybrid/Columbus, OH or Minneapolis, MN
Must Haves:
- 4 years of experience in combination of MLOps/DevOps/Data Engineering
- Bachelor's degree in Computer Science, Engineering, or a related discipline.
- Python: Deep expertise in Python for scripting and automation.
- AWS: Strong experience with AWS services, particularly SageMaker, S3, and Lambda.
- Terraform: Proficiency in using Terraform for infrastructure-as-code on AWS.
- Docker: Extensive experience with Docker, including building, managing, and securing Docker images.
- DevOps Experience: Azure DevOps (ADO): Significant experience in setting up and managing CI/CD pipelines in ADO.
- Proficient in using Git for version control and collaboration.
Plusses:
- Experience with large language models and productionizing ML models in a cloud environment.
- Exposure to near real-time inference systems and batch processing in ML.
- Familiarity with data drift and model drift management.
JOB DESCRIPTION
Location: Hybrid (Minneapolis, MN or Columbus, OH) - Remote for the duration of the contract, with an expectation of 3 days in the office per week once hired full-time. This is a hybrid role, with the expectation of being in the office three days a week once hired full-time. Candidates must be based in or willing to relocate to the Minneapolis, MN, or Columbus, OH areas. Remote work is possible for the duration of the 90-day contract, with the understanding that the role will transition to a hybrid model post-contract. Position not eligible for sponsorship now or in the future. Please make sure answers to screening questions are provided.
We are a forward-thinking team within a large enterprise bank, deeply invested in leveraging machine learning and artificial intelligence to drive impactful business outcomes. Our team is responsible for ensuring the smooth, scalable and secure deployment of machine learning models into production, handling both real-time and batch processing workloads. We offer a unique opportunity to work closely with data scientists and engineers, focusing on large language models and cutting-edge MLOps practices.
Job Summary:
As an MLOps Engineer, you will be responsible for the end-to-end productionization and deployment of machine learning models at scale. You will work closely with data scientists to refine models and ensure they are optimized for production. Additionally, you will be responsible for maintaining and improving our MLOps infrastructure, automating deployment pipelines, and ensuring compliance with IT and security standards. You will play a critical role in image management, vulnerability remediation, and the deployment of ML models using modern infrastructure-as-code practices.
Key Responsibilities:
1) Vulnerability Remediation & Image Management:
- Manage and update Docker images, ensuring they are secure and optimized.
- Collaborate with data scientists to validate that models run effectively on updated images.
- Address security vulnerabilities by updating and patching Docker images.
2) AWS & Terraform Expertise:
- Deploy, manage, and scale AWS services (SageMaker, S3, Lambda) using Terraform.
- Automate the spin-up and spin-down of AWS infrastructure using Terraform scripts.
- Monitor and optimize AWS resources to ensure cost-effectiveness and efficiency.
3) DevOps & CI/CD Pipeline Management:
- Design, implement, and maintain CI/CD pipelines in Azure DevOps (ADO).
- Integrate CI/CD practices with model deployment processes, ensuring smooth productionization of ML models.
- Strong experience with Git for code versioning and collaboration.
4) Model Productionization:
- Participate in the end-to-end process of productionizing machine learning models, from model deployment to monitoring and maintaining their performance.
- Work with large language models, focusing on implementing near real-time and batch inferences.
- Address data drift and model drift in production environments.
5) Collaboration & Continuous Learning:
- Work closely with data scientists, DevOps engineers, and other MLOps professionals to ensure seamless integration and deployment of ML models.
- Stay updated on the latest trends and technologies in MLOps, especially related to AWS and Docker.
