About the role

Role Overview

Senior MLOps Engineer (Remote, U.S.) responsible for building the core infrastructure and automation behind a production AI/ML environment. You will partner with Data Science, Platform Engineering, and Software Engineering to turn research models into scalable, reliable systems.

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 ML
Establish 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, Kubernetes, and 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

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

About Franklin Fitch

Franklin Fitch is recruiting for a high-growth technology company that is building a Machine Learning Platform function. The team operates at national scale and focuses on production AI infrastructure, enabling continuous training, deployment, and monitoring of ML models.