Your Data Scientists Build Great Models. None of Them Are in Production. Here's Why.
You have hired a team of brilliant data scientists. They are building models with impressive accuracy in their Jupyter notebooks. But those models rarely make it into your product. This is the "last mile" problem of machine learning, and it is the single biggest reason why most corporate AI initiatives fail to deliver a return on investment. The gap between a model in a notebook and a model serving live traffic is not a gap; it is a chasm.
MLOps is the discipline that bridges this chasm. It is the application of DevOps principles—automation, versioning, testing, and observability—to the unique lifecycle of machine learning systems. It is about building an "ML factory" that can reliably and repeatedly train, test, deploy, and monitor models. This is not a data science problem; it is a complex software and systems engineering problem.
When this critical function is staffed by data scientists with no production engineering experience, or by traditional DevOps engineers who don't understand the unique challenges of ML systems (like data versioning and model monitoring), your MLOps initiative is doomed to fail. You get brittle scripts, manual deployments, and models that silently decay in production. This playbook explains how Axiom Cortex finds the rare engineers who have the hybrid skillset to build a production-grade ML platform.
Traditional Vetting and Vendor Limitations
A nearshore vendor sees "MLOps" on a résumé, often next to "Kubernetes" and "Python," and assumes competence. The interview might involve asking the candidate to define CI/CD. This superficial approach completely fails to distinguish between an engineer who can set up a Jenkins job and an engineer who can design a reproducible training pipeline or a system for monitoring concept drift.
The predictable and painful results of this flawed vetting process are common:
- The "Manual Re-training" Nightmare: When a model needs to be re-trained on new data, a data scientist spends two days manually pulling data, running a training script on their laptop, and then asking a DevOps engineer to "please deploy this new model file." The process is slow, error-prone, and completely un-reproducible.
- Training-Serving Skew: A model performs brilliantly in training but fails in production because the feature engineering logic used during training is slightly different from the logic used for live prediction. There is no shared feature store or versioned feature pipeline to ensure consistency.
- Model Decay and "Silent" Failures: A model's predictive performance slowly degrades over time as the real-world data distribution shifts away from what it was trained on. No one notices for months, because there is no system in place to monitor the statistical properties of the model's inputs and predictions.
- The "It's Just a Pickle File" Deployment: A trained model is treated as a simple artifact. It's deployed without any versioning, without any record of the data it was trained on, or the code that produced it. When an auditor asks to explain why the model made a specific prediction, the team has no answer.
How Axiom Cortex Evaluates MLOps Engineers
Axiom Cortex is designed to find engineers who think about the ML lifecycle as an end-to-end automated system. We test for the practical skills in software engineering, infrastructure automation, and data science that are essential for building and operating a production ML platform. We evaluate candidates across four critical dimensions.
Dimension 1: The ML Lifecycle and Automation
This dimension tests a candidate's ability to design and build automated, repeatable pipelines for the entire ML lifecycle.
We provide candidates with a scenario (e.g., "Design an MLOps platform for a fraud detection model") and evaluate their ability to:
- Design a Training Pipeline: Can they design an automated pipeline that pulls data, preprocesses it, trains a model, evaluates it against a test set, and registers the model in a model registry if it meets a certain performance threshold? They should be familiar with tools like Kubeflow Pipelines, Airflow, or Vertex AI Pipelines.
- Implement CI/CD for ML (CI/CT/CD): A high-scoring candidate will talk about a "CI/CT/CD" process: Continuous Integration for code, Continuous Training for models, and Continuous Delivery for deployment. How do they version their data, their feature code, and their models?
Dimension 2: Model Deployment and Serving
Getting a model into production safely and efficiently is a core MLOps competency. This dimension tests a candidate's understanding of different model serving patterns and infrastructure.
We present a deployment scenario and evaluate if they can:
- Choose the Right Serving Pattern: Can they explain the trade-offs between online/real-time inference (e.g., via a REST API), batch inference, and streaming inference?
- Containerize a Model for Serving: Can they take a trained model file and package it into a secure, efficient Docker container for serving?
- Implement Progressive Delivery: How would they safely roll out a new version of a model? A high-scoring candidate will discuss strategies like canary releases or A/B testing to compare the new model's performance against the old one on live traffic.
Dimension 3: Monitoring and Observability
ML systems are not "set it and forget it." They require a unique kind of monitoring to detect when they start to fail. This dimension tests a candidate's knowledge of this critical area.
We evaluate their ability to design a system that can:
- Monitor for Data Drift and Concept Drift: Can they explain what these concepts are? Can they design a system to monitor the statistical distribution of the model's input features and its prediction outputs, and alert when they deviate significantly from the training data?
- Monitor for Performance and Cost: Can they design a system to monitor the latency, throughput, and cost-per-prediction of a deployed model?
Dimension 4: Infrastructure and Automation
An elite MLOps engineer is a strong infrastructure engineer. They know how to build the scalable and reliable platform that all ML workflows run on.
Axiom Cortex assesses how a candidate:
- Uses Infrastructure as Code (IaC): Are they proficient in using a tool like Terraform to define and manage the infrastructure for their ML platform (e.g., Kubernetes clusters, object storage, databases)?
- Understands Feature Stores: Do they understand the purpose of a feature store and how it can solve the problem of training-serving skew?
From a Science Lab to a Model Factory
When you staff your ML platform team with engineers who have passed the MLOps Axiom Cortex assessment, you are making a strategic investment in your ability to turn AI research into real business value.
A retail company had a data science team that was producing great models for product recommendations, but they had no way to deploy them. The process was manual, and it took months to get a single new model into production. Using the Nearshore IT Co-Pilot, we assembled an "MLOps Platform" pod of two elite nearshore MLOps engineers.
In their first quarter, this team:
- Built an Automated Training Pipeline: They built a CI/CD pipeline in Kubeflow that allowed data scientists to automatically trigger a model retraining and evaluation job whenever they pushed a change to their code.
- Implemented a Canary Deployment Strategy: They built a system that allowed a new model version to be safely rolled out to a small percentage of users, with its performance automatically compared to the current production model before a full rollout.
The result was transformative. The time to get a new model into production went from months to a few days. The data science team was able to iterate and experiment at a dramatically faster pace, leading to a 10% increase in the click-through rate on product recommendations.
What This Changes for CTOs and CIOs
Using Axiom Cortex to hire for MLOps competency is not about finding someone who knows Kubernetes. It is about insourcing the discipline of building an "AI factory"—a reliable, automated platform for turning data science into a repeatable, scalable business process.
It allows you to change the conversation with your CEO and your board. Instead of talking about AI as an expensive, high-risk research project, you can talk about it as a predictable and efficient engine for innovation. You can say:
"We have built an MLOps platform with a nearshore team that has been scientifically vetted for their ability to automate and operate production-grade machine learning systems. This platform allows us to turn our data science investments into business value faster and more reliably than our competitors."