About the role

Role overview

Senior MLOps Engineer for a remote (U.S.) position. You will help build scalable, reliable systems that power continuous training, deployment, and monitoring of ML models—turning research workflows into robust, repeatable production pipelines.

Responsibilities

Design, build, and maintain end-to-end ML pipelines (training, deployment, monitoring)
Develop scalable model-serving systems for both batch and real-time use cases
Implement CI/CD workflows for ML
Establish 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 engineering velocity

Requirements

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

Nice to have

Experience with feature stores or model registries
Monitoring/observability tooling
Streaming platforms such as Kafka or Kinesis
Terraform or other Infrastructure as Code (IaC) tools
Experience with LLM/GenAI-related pipelines

About Franklin Fitch

Franklin Fitch partners with high-growth technology companies to help them hire engineering talent. In this role, they support a company building a production-ready machine learning platform, with a focus on scalable infrastructure and end-to-end ML operations.