DevOps (MLOps) Engineer

Employment Type: Full-time

About the Engineering Organization

The Engineering Team at Loop is a balance of agility, consistency, and performance. These are the pillars that allow the team to constantly and consistently deliver value that matters to customers. That customer intimacy is what allows our engineering teams to be the best in our space, and bring the best ideas to the market.

About the Role

We are seeking a DevOps (MLOps) Engineer to pioneer and mature our machine learning operations capabilities, focusing on building robust infrastructure and deployment pipelines in AWS. This critical role will own the infrastructure underlying all of our productionalized ML models, from deployment to monitoring, fostering seamless collaboration between our machine learning and broader engineering teams. The MLOps engineer will work closely with ML engineers to keep state-of-the-art, mission-critical ML models up-to-date, scalable, and observable.

Our Blended Work Environment

At Loop, we’re intentional about the way we work so that we can do our best work. We call this our Blended Working Environment. We work from our HQ in Columbus, OH, or one of our Hub or Secluded locations, and are distributed throughout the United States, select Canadian provinces, and the United Kingdom.

Location: Columbus, OH; Chicago, IL; Austin, TX; Los Angeles, CA; or Fully Remote.

Our Tech Stack

• Cloud: AWS Cloud (Kubernetes, Serverless architecture, Redis, Aurora, DynamoDB, and identity management)
• Containerization: Docker
• MLOps: MLFlow, Airflow
• CI/CD: Gitlab
• Languages: PHP/Laravel
• OS: Linux
• IaC: Terraform
• Monitoring: Datadog
• Data: Snowflake, dbt

What You'll Do

• Design and implement scalable CI/CD pipelines for machine learning models within our AWS infrastructure, driving automated builds, deployments, and engineering excellence.
• Establish and evolve ML operational best practices in a greenfield environment, defining the standards for model versioning, reproducibility, and MLOps maturity.
• Collaborate closely with Machine Learning Engineers to understand model requirements and provide expert guidance on infrastructure, deployment strategies, and operationalizing their models.
• Implement and manage comprehensive monitoring and observability solutions for deployed ML models using tools like Datadog, ensuring high performance, accuracy, and quick issue resolution.
• Maintain and optimize ML model repositories to ensure efficient versioning and management of all model artifacts throughout their lifecycle.
• Drive the adoption of Infrastructure as Code (IaC) principles for ML infrastructure, ensuring reusability, consistency, and reliability across environments.
• Participate in the broader DevOps team ceremonies and planning, integrating ML Ops initiatives seamlessly into the overall engineering roadmap.

Your Experience

• 5+ years of experience in DevOps or MLOps Engineering roles, with at least 2+ years dedicated to hands-on experience specifically with machine learning operations, including enterprise-grade deep learning architectures (e.g., transformers, graph NNs, VAEs).
• Bachelor’s degree or higher in Computer Science, Mathematics, Statistics, or a related quantitative discipline, or equivalent practical experience, is highly preferred.
• Deep expertise in AWS infrastructure and services, with a proven track record of deploying and managing scalable ML workloads in the cloud.
• Strong proficiency in Python and extensive experience with key machine learning libraries such as PyTorch, Pandas, and scikit-learn.
• Extensive experience with containerization technologies (ex: Docker) for packaging and deploying ML models.
• Demonstrated experience with ML lifecycle management platforms such as MLflow.
• Proven ability to thrive as a self-starter in ambiguous, greenfield environments, taking initiative and delivering solutions with minimal oversight.
• Excellent collaboration and communication skills, with a demonstrated ability to act as a critical bridge between machine learning and broader engineering teams.