MLOps Engineer

Position Overview

• Location Type: Remote
• Employment Type: Full-Time
• Salary: (Salary not provided in the description)

Loopio is seeking a skilled and motivated MLOps Engineer to help scale and productionize the machine learning systems that power Loopio’s intelligent product features. This role involves building pipelines, infrastructure, and tooling to deliver high-impact ML models to users reliably, efficiently, and at scale. You’ll be a critical part of enabling our AI/ML roadmap, contributing to areas like intelligent search, content suggestions, document automation, and agent copilots.

Requirements

• 2+ years of experience in ML operations, ML engineering, or related infrastructure roles.
• Familiarity with deploying ML models and automating ML pipelines.
• Strong Python development skills with a solid understanding of software engineering practices (testing, logging, version control, code review).
• Comfort working with AWS (or similar cloud environments), Docker, and Kubernetes.
• Experience with workflow orchestration tools like Airflow, Dagster, or Kubeflow (a plus).
• Experience with model deployment & monitoring tools such as MLflow, SageMaker, TensorFlow Serving, or TorchServe.

Responsibilities

• Build and maintain robust ML pipelines for training, evaluation, and deployment.
• Automate routine workflows and support reproducible, auditable experimentation.
• Package and deploy models into production environments using tools like Docker, Kubernetes, and SageMaker.
• Build REST/gRPC services to serve models in real-time or batch.
• Implement systems to monitor model health in production, detect drift, and log predictions.
• Contribute to alerting and dashboarding to help the team maintain trust in deployed models.
• Work within CI/CD systems to support model validation, promotion, and rollback.
• Build safe, automated workflows for taking models from development to deployment.
• Partner with ML Engineers and Data Scientists to bring ML systems into production.
• Contribute to shared libraries and improve developer experience.
• Debug operational issues and partner with Infra and DevOps teams to understand their tooling.

Application Instructions

(Application instructions not provided in the description)