About the role

Role Overview

Senior MLOps Engineer (Remote, U.S.) joining a company’s Machine Learning Platform function. You will design and build scalable, reliable infrastructure and automation that powers the full ML lifecycle in production.

Responsibilities

Build and maintain end-to-end ML pipelines across training, deployment, and monitoring
Develop scalable model-serving systems for batch and real-time use cases
Implement CI/CD workflows for ML
Define standards for observability, reliability, and model governance
Automate retraining and model promotion workflows
Collaborate with Data Science, Platform Engineering, and Software Engineering to improve platform performance and delivery velocity

Requirements

Strong Python engineering background
Hands-on experience with Docker and Kubernetes
Experience with cloud platforms: AWS, GCP, or Azure
Experience with ML workflow tools such as: MLflow, Kubeflow, SageMaker, Vertex, Airflow, Dagster, Prefect
Strong understanding of model deployment, distributed systems, and data pipelines
Practical experience building production ML systems

Nice to Have

Familiarity with feature stores or model registries
Experience with monitoring/observability tooling
Experience with streaming platforms such as Kafka or Kinesis
Experience with Terraform or other Infrastructure as Code (IaC) tools
Exposure to LLM/GenAI pipelines

About Franklin Fitch

Franklin Fitch is a recruiting firm partnering with high-growth technology companies to help them hire engineering talent. This role is with a well-funded, engineering-first business building a production-ready machine learning platform at national scale.