Automate Your Intelligence with a Machine Learning Engineer
Your competitors are already automating their core business decisions. Are you?
You know that artificial intelligence (AI) and machine learning (ML) are the essential infrastructure of modern profitability. They power recommendation engines, automate fraud detection, and predict crucial market shifts.
Yet, can your business justify sinking massive capital into a rigid salary structure, or can you afford the crippling opportunity cost of not building that predictive intelligence? You’re leaving millions in revenue on the table by relying on manual processes and generic software.
You need to secure a dedicated, world-class engineer who can build and deploy production-ready ML models that automate your growth. Stop wishing for intelligence.
Partner with Outsourced Staff for a dedicated machine learning engineer.
Many organisations report that a lack of skilled professionals is the main barrier to adopting machine learning at scale.
Think about the time difference between having an idea for a predictive model and actually deploying it into your live system. That gap is where your profit dies.
Machine learning engineers are the only ones who can bridge that gap, turning statistical insights into scalable software solutions. And outsourcing allows you to tap into that expertise when you need it.
This shortage slows innovation and increases project failure rates.
Outsourcing helps you overcome that challenge by giving you immediate access to experienced machine learning engineers who can move projects forward without delay.
This ensures your AI projects and overall systems are delivered quickly, securely, and scale effortlessly with your business.
Outsourced Machine Learning Engineer Roles
Outsourced Staff provides a range of artificial intelligence and automation solutions:
Data Preparation and Feature Engineering
- Data Preprocessing Engineer
- Feature Engineering Specialist
- Data Quality Analyst
- Dataset Validation Engineer
- Data Pipeline Engineer
- Data Reporting Manager
Model Development and Deployment
- Machine Learning Engineer
- Model Training Specialist
- Algorithm Development Engineer
- Model Deployment Engineer
- MLOps Engineer
Monitoring, Optimisation, and Insights
- Model Performance Analyst
- Prediction Accuracy Analyst
- Model Optimisation Engineer
- Experimentation Analyst
- Machine Learning Support Engineer
Find the skilled pros to support your machine learning initiatives!
Boost AI Deployment with Outsourced Staff
Machine learning projects fail when they lack focus, expertise, or continuity. Internal teams get stretched. Contractors disappear mid-project. Results never reach production.
Outsourced Staff helps you avoid these issues by providing dedicated engineers who work as an extension of your team.
We match you with professionals who understand both data science and business impact. They follow your processes, communicate clearly, and focus on delivering usable outcomes rather than academic experiments.
- Elite Expertise, Low Cost. Benefit from an around 70% reduction in employment costs compared to a local ML engineer’s salary.
- Guaranteed Deployment. Access specialists focused purely on MLOps and production readiness.
- Cloud & Code Mastery. Hire experts fluent in Python, cloud ML services, and Kubernetes for scalable operations.
- Rapid Scale. Quickly onboard engineers for project-based initiatives without the massive time drag of local recruitment.
- Focus on Strategy. Free up your data science team to focus on research and algorithm design, while the engineer handles deployment.
Accelerate Your Growth by Outsourcing ML Engineers
Machine learning only delivers value when it moves from concept to execution. With the right engineers in place, your data becomes a strategic asset rather than an untapped resource.
Start building your machine learning capability today. Outsourced Staff will help you find the right engineers to accelerate innovation and drive measurable results.
Want to grow faster? Outsourcing is for you.
When you outsource staffing, you reap the benefits of a dedicated, results-driven team without getting bogged down in day-to-day operations.
So you can easily increase efficiency, and scale your IT or digital business.
With an outsourced team you get:
- A high-performing dedicated team that integrates into your business
- Full visibility and control over team’s workflow, processes, KPIs and delivery
- Fast, reliable recruitment
- Flexible agreements and lower costs
- Your team’s HR, payroll, time off and more, taken care of
- Ongoing support for your team to improve reporting, productivity and loyalty to your business
Frequently Asked Questions
What does an outsourced machine learning engineer do?
Outsourced machine learning engineers design, train, and deploy machine learning models that help your business automate processes, predict outcomes, and uncover insights from data.
What is the biggest difference between a data scientist and a machine learning engineer?
The primary difference between a data scientist and a machine learning engineer is the focus on production. A data scientist focuses on research and building the statistical model (the algorithm).
A machine learning engineer focuses on software engineering: Cleaning the data pipeline, packaging the model, and deploying it reliably into the live, production environment using MLOps principles.
How does an outsourced ML engineer ensure model reliability and prevent 'model drift'?
An ML engineer ensures reliability by implementing robust MLOps monitoring. They use specialised tools to monitor the deployed ML model’s performance in real-time.
This helps detect model drift (when the accuracy declines due to real-world data changing) and automatically triggers alerts for necessary retraining or maintenance, ensuring the AI system remains accurate and effective.
Can an outsourced ML engineer handle deployment on major cloud platforms like AWS or Azure?
Yes. Proficiency in cloud platforms is essential for modern ML engineering. Our specialists are experienced in deploying and managing models on major cloud services like AWS SageMaker, Google AI Platform, or Azure Machine Learning.
They leverage these platforms for scalable training, efficient hosting, and integrating the resulting model via REST APIs into your applications.
What are the security considerations when using a remote machine learning engineer with our data?
Security is handled via strict protocol and infrastructure control. The machine learning engineer works within your existing, secure cloud environment and is bound by comprehensive Non-Disclosure Agreements (NDAs).
Their access is limited and monitored, focusing on the data pipelines and infrastructure, ensuring your proprietary data remains secured within your control system throughout the entire MLOps lifecycle.
