Hire Machine Learning Developers
Need a dedicated machine learning developer? Xirgosoft developers can join your team or build your project end to end.
You get an experienced engineer who follows clear processes, communicates regularly and writes clean, maintainable code.
Tell us about your project and we will recommend the right developer and way of working.
What to Expect
Machine learning developers build models that learn from your data to predict, rank, classify or detect. Typical business uses include lead scoring, churn prediction, demand forecasting, fraud or anomaly detection and recommendation.
Real projects spend most of their time on data: finding it, cleaning it, defining the target and engineering useful features. Our ML developers start there, then train and compare models using tools such as scikit-learn, PyTorch or TensorFlow, evaluate them honestly on data the model has not seen and explain what drives the predictions. We prefer a simple model that works to a complex one that does not.
Delivery matters as much as accuracy. We deploy models as APIs or scheduled jobs, version data and models, monitor for drift and set up retraining so results stay useful. If your problem is better solved with rules, a database query or a language model, we will say so.
What You Get
Experienced Engineers
Developers with hands-on machine learning project experience.
Clean, Maintainable Code
Readable code with documentation and handover.
Regular Communication
Progress updates and demos you can follow.
Your Tools and Process
We work with your repository, tracker and workflow.
Full Code Ownership
You own the code and have repository access.
Flexible Scale
Add or reduce capacity as your project changes.
What Your Developer Can Take On
Predictive Models
Forecast demand, churn, conversion or risk from historical data.
Data Preparation
Clean, join and engineer features from CRM, ERP and other sources.
Model Evaluation
Validate models with proper metrics and held-out data.
Deployment
Serve models through APIs or batch jobs inside your systems.
Monitoring and Retraining
Detect drift and keep models current.
Explainability
Show which factors drive predictions so users can trust them.
Skills & Tools
Ideal Projects
- Lead scoring and sales forecasting
- Customer churn and retention analysis
- Demand and inventory forecasting
- Anomaly and fraud detection
- Recommendation or ranking features
How We Work Together
- Clear scope, priorities and a shared task board
- Regular progress updates and working demos
- Code in your repository with reviews and handover notes
- Communication on the channels your team already uses
- Flexible capacity that can grow or shrink with the project
- Full code ownership and documentation
Ways to Work With Us
Dedicated Developer
A developer focused on your project, working as part of your team.
Part-Time Developer
Reduced hours for smaller projects or ongoing maintenance.
Fixed-Scope Project
We deliver a defined scope for an agreed price and timeline.
Problems We Help You Solve
Messy or Sparse Data
We assess data quality early and recommend what to collect or fix.
Models That Never Ship
We design for deployment from the beginning, not as an afterthought.
Overfitting
We validate carefully so results hold up on new data.
Lack of Trust
We explain predictions and compare against simple baselines.
How It Works
Share Requirements
Tell us your goals, stack and timeline.
Match & Review
We recommend a developer and you review the fit.
Onboard
We set up access, tools and communication.
Deliver & Review
Work proceeds in sprints with regular reviews.
Frequently Asked Questions
How quickly can a machine learning developer start?
It depends on current availability and your requirements. Contact us and we will confirm a start date.
Can I talk to the developer first?
Yes. We can arrange a conversation so you are comfortable with the fit before you commit.
Who owns the code?
You receive full ownership of the code and access to the repository.
Can you handle the whole project instead?
Yes. We can build and deliver the project end to end.
Do we need a lot of data?
It depends on the problem. We assess what you have and recommend realistic approaches.
Which tools do you use?
Python with scikit-learn, PyTorch, TensorFlow and standard data tooling, chosen to fit the problem.
How do you deploy models?
As APIs or scheduled jobs integrated with your systems, with monitoring.
Can ML models feed our CRM?
Yes. Scores and predictions can be written back to vTiger and other CRMs.
Related Services
Hire a machine learning developer today.
Talk to our experts today for a free consultation and quote.