MLOps Engineer II — Hybrid with AWS SageMaker Pipelines

IGH ICW GROUP HOLDINGS, INC.; Baxter Village (SC); Hybrid; USD 106,000 - 189,000; Full time

Job summary

ICW Group is seeking an MLOps Engineer II to design, build, and run scalable ML pipelines on AWS. You will collaborate with data scientists and cloud engineers to productionize models, implement CI/CD, and optimize ML infrastructure costs in a hybrid office setting.

You will apply advanced software engineering practices, work with IaC tools, and help ensure secure, reliable ML systems across production environments.

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 engineering, DevOps, cloud engineering, or MLOps roles.
• Strong programming experience in Python for building automation, services, or data processing pipelines.
• Hands-on experience with AWS cloud services, including SageMaker, Lambda, Step Functions, S3, IAM, and CI/CD tools.
• Experience designing and deploying machine learning models into production environments.
• Experience building and maintaining CI/CD pipelines and automated deployment workflows.
• Experience working with Infrastructure-as-Code tools such as CloudFormation, Terraform, or AWS CDK.
• Strong troubleshooting and problem-solving skills in distributed or cloud-based systems.
• Experience collaborating with cross-functional teams including data science, engineering, and business stakeholders.

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.

Skills

Python

AWS

SageMaker

CI/CD

Terraform

CloudFormation

AWS CDK

Docker

ECS

EKS

FinOps

Education

Bachelor’s degree in Computer Science, Engineering, Data Science, or related field

Tools

SageMaker

Lambda

Step Functions

S3

Job description

ICW Group is seeking an MLOps Engineer II to design, build, and run scalable ML pipelines on AWS. You will collaborate with data scientists and cloud engineers to productionize models, implement CI/CD, and optimize ML infrastructure costs in a hybrid office setting.

You will apply advanced software engineering practices, work with IaC tools, and help ensure secure, reliable ML systems across production environments.