Hybrid - Junior ML OPS Engineer

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.

Pay Rate Range: 40-50/hr some flexibility depending on experience

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal. com.

To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/ . 2+ 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.