About the role

Role Overview

Senior MLOps Engineer for a remote (U.S.) role, owning key parts of a production AI/ML environment. You’ll design scalable, reliable infrastructure and automation that power the end-to-end ML lifecycle—continuous training, deployment, and monitoring—working closely with Data Science and engineering teams.

Responsibilities

Build and maintain end-to-end ML pipelines (training, deployment, 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 cross-functionally to improve platform performance and engineering velocity

Requirements

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

Nice to Have

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 pipelines

About Franklin Fitch

Franklin Fitch is a technology-focused recruitment partner supporting high-growth engineering teams. They are working with a well-funded company building a production Machine Learning Platform, including scalable infrastructure for training, deployment, and monitoring of ML models.