About the role

Senior MLOps Engineer

Remote (U.S.) | Permanent | $160,000 - $220,000 base + benefits

Role Overview

Own and build core infrastructure for the company’s production AI environment. You’ll design scalable, reliable systems that enable continuous training, deployment, and monitoring of ML models, working closely with Data Science, Platform Engineering, and Software Engineering.

Key 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
Set 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, or 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
Monitoring/observability tooling experience
Streaming platforms such as Kafka or Kinesis
Terraform or other IaC tools
Experience with LLM/GenAI pipelines

About Franklin Fitch

Franklin Fitch is a technology-focused recruitment partner that supports hiring for high-growth engineering organizations. In this role, they are representing a company building a scalable Machine Learning Platform, with a focus on production AI infrastructure.

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