What are the responsibilities and job description for the MLOps Engineer position at Compunnel Inc.?
Job Location:
Columbus, OH or Minneapolis, MN
About Us
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 product ionization 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
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.
AWS & Terraform Expertise
Deploy, manage, and scale AWS services (Sage Maker, 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.
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 product ionization of ML models.
Strong experience with Git for code versioning and collaboration.
Model Product Ionization
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.
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.
Required Skills & Qualifications
Python: Deep expertise in Python for scripting and automation.
AWS: Strong experience with AWS services, particularly Sage Maker, 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.
Linux: Strong command-line skills in Linux, especially for Docker and system management.
DevOps Experience: Azure DevOps (ADO): Significant experience in setting up and managing CI/CD pipelines in ADO.
Git: Proficient in using Git for version control and collaboration.
Additional DevOps Tools: Experience with Jenkins or other CI/CD tools is a plus.
Experience & Education
4 years of experience in combination of MLOps/DevOps/Data Engineering.
Bachelor's degree in Computer Science, Engineering, or a related discipline.
Preferred Qualifications
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.
Primary Skills
MLOps, DevOps, AWS, Docker, Git Version Control, python, SQL, Terraform
Education: Bachelors Degree
Columbus, OH or Minneapolis, MN
About Us
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 product ionization 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
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.
AWS & Terraform Expertise
Deploy, manage, and scale AWS services (Sage Maker, 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.
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 product ionization of ML models.
Strong experience with Git for code versioning and collaboration.
Model Product Ionization
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.
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.
Required Skills & Qualifications
Python: Deep expertise in Python for scripting and automation.
AWS: Strong experience with AWS services, particularly Sage Maker, 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.
Linux: Strong command-line skills in Linux, especially for Docker and system management.
DevOps Experience: Azure DevOps (ADO): Significant experience in setting up and managing CI/CD pipelines in ADO.
Git: Proficient in using Git for version control and collaboration.
Additional DevOps Tools: Experience with Jenkins or other CI/CD tools is a plus.
Experience & Education
4 years of experience in combination of MLOps/DevOps/Data Engineering.
Bachelor's degree in Computer Science, Engineering, or a related discipline.
Preferred Qualifications
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.
Primary Skills
MLOps, DevOps, AWS, Docker, Git Version Control, python, SQL, Terraform
Education: Bachelors Degree
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