Job Description & Details
This is a heavy-duty MLOps contract role focused on building scalable machine learning platforms and production ML systems for a large enterprise environment in Cincinnati. You won't just be dropping models into a notebook; you will be architecting the entire end-to-end lifecycle infrastructure, from CI/CD/CT pipelines to real-time inference and strict model governance.
What You'll Actually Be Doing
Your day-to-day will revolve around bridging the gap between data science and production software engineering. You'll spend a lot of time automating deployment pipelines, setting up feature stores, and making sure models don't silently degrade in production due to data or concept drift. Expect to collaborate heavily with data scientists who might write great Python code, but need your expertise to containerize, orchestrate, and scale those models securely in the cloud.
The Core Tech Stack
You need to be fluent in Python and SQL, with deep hands-on experience in cloud ML platforms like AWS SageMaker, Azure ML, or Google Vertex AI. Kubernetes, Docker, and Infrastructure-as-Code tools like Terraform are non-negotiable here, as you'll be managing containerized, low-latency scoring platforms backed by streaming technologies like Apache Kafka and data platforms like Databricks or Snowflake.
Interview Expectations
Be ready for deep-dive questions around handling concept drift and how you would architect a real-time, low-latency scoring pipeline that fails gracefully under heavy load. The hiring manager is secretly testing whether you understand the operational friction points of ML systems, so focus your answers on reliability, automated rollback strategies, and monitoring observability metrics using Prometheus and Grafana rather than just raw model accuracy.
Application Advice
Make sure your resume doesn't just read like a standard data science or DevOps CV. You need to explicitly highlight the intersection of both worlds by sprinkling in exact keywords from the description like 'CI/CD/CT pipelines', 'Feature Store architecture', 'model drift monitoring', and 'Kubernetes'. Frame your past projects around how you reduced deployment friction and automated retraining loops for production workloads.