Do your ML engineers handle both training and deployment?

Yes. Our ML engineers own the full lifecycle from data preparation and model training to production deployment and monitoring.

What frameworks do they use?

Our ML engineers work with PyTorch, TensorFlow, scikit-learn, and MLOps tools like MLflow, Kubeflow, and SageMaker.

Can they optimize model inference speed?

Absolutely. Our engineers apply quantization, pruning, ONNX conversion, and hardware-specific optimizations for low-latency inference.

Hire a Machine Learning Engineer

Get a pre-vetted ML engineer for model training, MLOps pipelines, and production-grade machine learning systems — AI-managed delivery.

Role: Machine Learning Engineer (Data)

ML engineers bridge the gap between data science and production systems. Our vetted ML talent builds training pipelines, deploys models at scale, implements MLOps best practices, and optimizes inference performance for real-time applications.

Skills We Vet

Model Training & Optimization: Expert
MLOps (MLflow, Kubeflow): Advanced
Deep Learning (PyTorch, TensorFlow): Advanced
Model Serving & Inference: Advanced

Typical Projects

ML Pipeline: End-to-end ML pipeline with data preprocessing, training, evaluation, and automated retraining. (100-200 hrs)
Model Deployment: Deploy trained models as scalable APIs with monitoring, A/B testing, and rollback capabilities. (60-120 hrs)
Computer Vision System: Image classification or object detection system with training, validation, and edge deployment. (120-250 hrs)

Hourly Rates

AI PM: $2/hr — AI agent manages the project end-to-end with automated code reviews, testing, and deployment.
Live PM: $3/hr — A human project manager coordinates your project with AI-augmented development workflows.
Live PM + Dev: $5/hr — Dedicated human PM plus senior developer oversight for mission-critical projects.

Hiring Process

Submit Your Requirements: Describe your project scope, technical needs, and timeline. Our AI analyzes your requirements and identifies the ideal skill profile.
AI-Matched Talent Selection: Our platform matches you with pre-vetted developers whose expertise aligns with your tech stack, industry, and project complexity.
Technical Vetting & Trial: Review candidate profiles, past work, and skill assessments. Start with a small paid trial task to validate the fit before committing.
Kick-off & Ongoing Delivery: Once confirmed, your developer is onboarded immediately. Track progress via real-time dashboards, milestone reviews, and daily stand-ups.

Frequently Asked Questions

Do your ML engineers handle both training and deployment?: Yes. Our ML engineers own the full lifecycle from data preparation and model training to production deployment and monitoring.
What frameworks do they use?: Our ML engineers work with PyTorch, TensorFlow, scikit-learn, and MLOps tools like MLflow, Kubeflow, and SageMaker.
Can they optimize model inference speed?: Absolutely. Our engineers apply quantization, pruning, ONNX conversion, and hardware-specific optimizations for low-latency inference.