Hire MLOps Engineers | TeamStation AI

MLOps is the discipline of bringing DevOps principles to Machine Learning. Our MLOps Engineers automate and streamline the end-to-end machine learning lifecycle, from data ingestion and model training to deployment and monitoring. We provide experts who can build a reliable and scalable platform for your data science teams, enabling them to ship models faster and with greater confidence.

Is your 'model deployment' a manual, multi-week process?

The Problem

Without an MLOps platform, deploying a new model is a slow, manual, and risky process involving data scientists, software engineers, and operations. This bottleneck prevents the business from benefiting from the latest model improvements.

The TeamStation AI Solution

Our MLOps engineers are experts in CI/CD for Machine Learning. They are vetted on their ability to build automated pipelines for model training, validation, and deployment using tools like Kubeflow, MLflow, and cloud-native services. This reduces deployment time from weeks to hours.

Proof: Fully automated model training and deployment pipelines.
Is model performance a black box after deployment?

The Problem

Models degrade over time due to data drift. Without robust monitoring, you have no visibility into a model's real-world performance, leading to 'silent failures' that can negatively impact business outcomes and erode trust in AI initiatives.

The TeamStation AI Solution

We provide experts in production model monitoring. They implement automated systems to track model accuracy, data drift, and fairness metrics, with alerts to trigger retraining pipelines before performance degradation affects users. This ensures your models remain reliable and effective over time.

Proof: Comprehensive model monitoring and automated drift detection.

How We Measure Seniority: From L1 to L4 Certified Expert

We don't just match keywords; we measure cognitive ability. Our Axiom Cortex™ engine evaluates every candidate against a 44-point psychometric and technical framework to precisely map their seniority and predict their success on your team. This data-driven approach allows for transparent, value-based pricing.

L1 Proficient

Guided Contributor

Contributes on component-level tasks within the MLOps Engineer domain. Foundational knowledge and learning agility are validated.

Evaluation Focus

Axiom Cortex™ validates core competencies via correctness, method clarity, and fluency scoring. We ensure they can reliably execute assigned tasks.

$20 /hour

$3,460/mo · $41,520/yr

± $5 USD

L2 Mid-Level

Independent Feature Owner

Independently ships features and services in the MLOps Engineer space, handling ambiguity with minimal supervision.

Evaluation Focus

We assess their mental model accuracy and problem-solving via composite scores and role-level normalization. They can own features end-to-end.

$30 / hour

$5,190/mo · $62,280/yr

± $5 USD

L3 Senior

Leads Complex Projects

Leads cross-component projects, raises standards, and provides mentorship within the MLOps Engineer discipline.

Evaluation Focus

Axiom Cortex™ measures their system design skills and architectural instinct specific to the MLOps Engineer domain via trait synthesis and semantic alignment scoring. They are force-multipliers.

$40 / hour

$6,920/mo · $83,040/yr

± $5 USD

L4 Expert

Org-Level Architect

Sets architecture and technical strategy for MLOps Engineer across teams, solving your most complex business problems.

Evaluation Focus

We validate their ability to make critical trade-offs related to the MLOps Engineer domain via utility-optimized decision gates and multi-objective analysis. They drive innovation at an organizational level.

$50 / hour

$8,650/mo · $103,800/yr

± $10 USD

Pricing estimates are calculated using the U.S. standard of 173 workable hours per month, which represents the realistic full-time workload after adjusting for federal holidays, paid time off (PTO), and sick leave.

Core Competencies We Validate for MLOps Engineer

CI/CD for Machine Learning (MLflow, Kubeflow)
Infrastructure as Code for ML (Terraform, CloudFormation)
Model Serving & Inference Infrastructure (KServe, Seldon)
Model Monitoring and Drift Detection
Feature Stores and Data Versioning

Our Technical Analysis for MLOps Engineer

Candidates must design and implement a complete CI/CD pipeline for an ML model. This includes automating the training process, versioning the model in a registry like MLflow, and deploying it as a scalable inference service on Kubernetes. We assess their ability to implement monitoring to detect data and model drift in production.

Related Specializations

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About TeamStation AI

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Nearshore vs. Offshore

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