About the role

Role overview

Senior MLOps Engineer (Remote, U.S.) working at the core of a production AI/ML environment. You’ll design and build scalable, reliable infrastructure and automation that power the end-to-end ML lifecycle, partnering with Data Science, Platform Engineering, and Software Engineering.

Responsibilities

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 machine learning
Define standards for observability, reliability, and model governance
Automate retraining and model promotion workflows
Collaborate cross-functionally 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
Experience with ML workflow tools (e.g., 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

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

About Franklin Fitch

Franklin Fitch is a technology recruitment partner that supports high-growth companies in building engineering teams. In this role, they are partnering with a company focused on production-grade machine learning platforms, helping scale continuous training, deployment, and monitoring for AI models.