[TECH.DEV]
MLOps Engineer
Extended Desk provides expert MLOps engineers who manage the lifecycle of your machine learning models. We focus on automating deployment processes, ensuring consistent performance, and maintaining the infrastructure that allows your models to function at scale.
Our approach integrates engineering rigor into your AI development, allowing your core team to prioritize model architecture and high-level research while we handle the operational requirements of deployment and monitoring.
The problem
Translating successful model prototypes into consistent, production-ready services often stalls when organizations lack dedicated operations capacity. Without established CI/CD for ML, data drift management, or reliable infrastructure automation, your technical team spends excessive time on manual updates and debugging, delaying model releases and increasing the risk of production failure.
The solution
Extended Desk deploys dedicated MLOps engineers who implement automated model lifecycles and infrastructure as code. We establish the standardized environments, monitoring cycles, and versioning protocols necessary to keep your AI systems reliable and updated without diverting your primary development team from their high-value research initiatives.
The Challenge
Manual deployment processes leading to inconsistent model performance
Lack of monitoring causing undetected data drift in live environments
Scaling AI infrastructure to handle modern compute requirements
Difficulty bridging the gap between data science experimentation and production reliability
Lower cost
than local hire
Save up to compared to hiring locally
Fixed monthly rates. No hidden fees for equipment, benefits, or overhead.
What you
actually get.
Pipeline automation
Dedicated specialists who build and manage CI/CD pipelines specifically for ML, ensuring code, data, and model versioning remain synchronized.
Operational reliability
Engineers who monitor model performance and data drift in real-time to trigger automated retraining cycles before issues impact the end user.
Infrastructure management
Experts who handle containerization and orchestration, managing your cloud environments to ensure your models are highly available and scalable.
Feature store oversight
Dedicated talent to organize and maintain feature stores, providing consistent data access across your team for both training and inference.
Increased deployment frequency
Infrastructure-as-code practices that reduce the time between successful model training and full production deployment.
How we
build it.
Every engagement follows a structured lifecycle designed to turn your need into a high-performing, continuously improving operation.
Discover
We audit your existing machine learning workflows, current tech stack, and deployment challenges to map out the required operational improvements.
Design
We outline the infrastructure requirements and automation strategies, defining roles for your dedicated engineers and how they integrate with your internal teams.
Recruit
We source technical talent with specific experience in containerization, cloud AI platforms, and automated ML lifecycles to match your project requirements.
Onboard
Your engineers are integrated into your environment, gaining access to your repositories, security protocols, and development workflows to begin contributing immediately.
Operate
The team maintains production uptime, monitors model health, and manages CI/CD cycles according to the defined SLAs and performance metrics.
Uplift
We conduct regular reviews to identify opportunities for further automation, performance tuning, and scaling your AI capabilities as your data needs grow.
Ready to extend?
Our MLOps engineers provide the technical stability needed to transform experimental models into reliable, production-ready AI services.