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An ML engineer at Cloudrix bridges data science and software engineering. They take models from notebooks to production with proper training pipelines, feature stores, model serving, and monitoring. They implement MLOps practices including experiment tracking, model versioning, automated retraining, and A/B testing infrastructure.
Describe your ML use case, current model status, and production requirements.
Within 48 hours, we present an ML engineer with experience in your domain and ML framework.
Your engineer evaluates the current approach, identifies improvements, and builds production-ready pipelines.
Automated training pipelines, model serving, monitoring, and continuous improvement.
Data scientists explore data and develop models. ML engineers put those models into production with proper engineering practices. Often you need both, but the ML engineer ensures models actually deliver value.
SageMaker (AWS), Vertex AI (GCP), and Azure ML depending on your cloud provider. They also build custom pipelines with Kubeflow or Ray when managed services are not sufficient.
Yes. Our ML engineers handle LoRA fine-tuning, RLHF, and custom model training for domain-specific LLM applications.
Tell us what you need and we will match you with the right engineer within 48 hours. Start with a risk-free trial month.