MLOps Engineer II

Are you looking to make an impactful difference in your work, yourself, and your community? Why settle for just a job when you can land a career? At ICW Group, we are hiring team members who are ready to use their skills, curiosity, and drive to be part of our journey as we strive to transform the insurance carrier space. We're proud to be in business for over 50 years, and its change agents like yourself that will help us continue to deliver our mission to create the best insurance experience possible.

Headquartered in San Diego with regional offices located throughout the United States, ICW Group has been named for the twelfth consecutive year in a row and for the 20th time overall as a Top 50 performing P&C organization offering the stability of a large, profitable and growing company combined with a focus on all things people. It's our team members who make us an employer of choice and the vibrant company we are today.

We strive to make both our internal and external communities better everyday! Learn more about why you want to be here! PURPOSE OF THE JOB The MLOps Engineer II is responsible for designing, developing, and operating scalable machine learning infrastructure and deployment pipelines on AWS. The MLOps Engineer II works closely with data scientists, cloud engineers, and application teams to productionize machine learning models and ensure reliable, secure, and cost-efficient ML operations.

This position applies advanced software engineering and cloud development practices to automate machine learning workflows, optimize infrastructure utilization, and maintain production ML systems. This role requires strong coding skills, experience working with ML systems in production, and the ability to independently implement technical solutions that support the organization's AI and analytics initiatives.

ESSENTIAL DUTIES AND RESPONSIBILITIES Design, develop, and maintain scalable machine learning pipelines using AWS services such as SageMaker, Lambda, Step Functions, and S3. Build and manage deployment frameworks for machine learning models in real-time and batch inference environments. Develop and maintain Python-based tools and services for data processing, model packaging, and ML pipeline orchestration.

Design and implement CI/CD pipelines for machine learning systems using GitHub and AWS development tools. Develop and manage infrastructure components using Infrastructure-as-Code tools such as AWS CloudFormation, Terraform, or AWS CDK. Implement monitoring, logging, and alerting solutions to ensure reliability and observability of ML systems in production. Troubleshoot and resolve complex issues in ML development and production environments.

Partner with data scientists and engineering teams to integrate machine learning models into enterprise applications and data platforms. Lead implementation of AI/ML FinOps best practices, analyzing resource usage and optimizing compute, storage, and infrastructure costs for ML workloads. Monitor AWS usage, budgets, and cost trends related to ML infrastructure and implement optimization strategies to improve cost efficiency.

Improve automation, reliability, and scalability of ML pipelines and operational workflows. Ensure ML systems comply with enterprise security, governance, and regulatory standards in coordination with Information Security teams. Participate in architectural discussions and contribute to technical standards for MLOps and ML infrastructure. Provide technical guidance and mentorship to junior engineers and contribute to knowledge sharing within the team.

Conduct code reviews and promote best practices in software engineering, testing, and deployment. SUPERVISORY RESPONISBILITIES This role does not have supervisory responsibilities. REQUIRED QUALIFICATIONS Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field or equivalent combination of education and experience. Minimum 3 - 5 years of experience in software engineerin