Hire Machine Learning Engineers
Hire vetted Machine Learning Engineers through Hevcode: fully remote, starting in 48 hours, with timezone-overlap working hours and a risk-free trial. 534+ projects shipped over 6 years.
Get skilled ML engineers who take models from notebook to production and keep them running. TensorFlow, PyTorch, MLOps, serving. Start within 48 hours.
Prefer email? Reach me at contact@hevcode.com.
534+ projects delivered | 273+ verified reviews | Start in 48 hours
Last updated: June 2026
Looking to hire machine learning engineers who can ship, not just train? Our ML engineers own the full lifecycle: training, evaluation, deployment, monitoring, and retraining, so your model creates value in production instead of dying in a notebook.
The hard part of ML in production isn't the model, it's everything around it: reproducible training, feature consistency between training and serving, low-latency inference, drift detection, and rollback when a new model regresses. Most data scientists stop at the model file. Our ML engineers handle the entire path to a reliable, observable service.
From a first model deployment to a full MLOps platform, we offer flexible engagement models to match your stage and budget. Developers start within 48 hours, backed by a 1-week risk-free trial.
Technical Skills
Our developers are proficient in these technologies and more
ML Frameworks
- PyTorch
- TensorFlow / Keras
- scikit-learn
- Hugging Face Transformers
- ONNX
- JAX
Training & Optimization
- Distributed training
- Hyperparameter tuning
- Model quantization & pruning
- Mixed-precision training
- GPU/CUDA optimization
- Transfer learning & fine-tuning
Serving & Feature Infra
- Model serving (TorchServe, TF Serving, Triton)
- Feature stores (Feast)
- Real-time & batch inference
- Model registry (MLflow)
- Vector databases
- Low-latency APIs
MLOps & DevOps
- MLflow / Kubeflow
- CI/CD for ML
- Docker & Kubernetes
- Drift & performance monitoring
- Experiment tracking (Weights & Biases)
- Cloud ML (SageMaker, Vertex AI)
Why Hire Through Us
Benefits of hiring developers through Hevcode
Pre-Vetted Experts
Every ML engineer is tested on modeling and production deployment, with proven experience serving models at scale.
Quick Onboarding
Start working with your ML engineer within 48 hours. No prolonged hiring process.
Flexible Engagement
Hire to deploy a single model, own an ongoing ML platform, or build full MLOps from scratch. Scale as needed.
Direct Communication
Work directly with your ML engineer on metrics, latency targets, and deployment plans. No middlemen.
Timezone Overlap
At least 4 hours of overlap with your team for model reviews and deployment coordination.
Risk-Free Trial
Start with a 1-week trial. If the work or fit isn't right, no payment required.
Engagement Models
Flexible hiring options to match your needs
Dedicated Developer
A full-time ML engineer owning training, deployment, and monitoring for your models. Best for companies building ML into the product.
Ideal for: Startups shipping ML features, companies productionizing existing models
Development Team
A complete squad of ML engineers, a data engineer, and a data scientist with project management. Fully managed delivery from data to served model.
Ideal for: Complex ML systems, enterprises building an MLOps platform
Hourly/Part-Time
Flexible hours to deploy a model, fix an inference bottleneck, or review an ML architecture. Pay only for hours worked.
Ideal for: Deployments, latency tuning, MLOps audits, advisory
Hiring Process
Simple 4-step process to get your developer
Share Requirements
Tell us about your models, data, latency targets, and deployment environment. We'll understand your ML maturity and infrastructure.
Developer Matching
Within 24 hours, we'll present 2-3 pre-vetted ML engineers matched to your frameworks (PyTorch, TensorFlow) and serving needs, with profiles and availability.
Interview & Select
Interview the candidates, review past deployments and how they handle drift and rollback, and select your preferred engineer. We handle logistics.
Start Building
Your ML engineer joins within 48 hours. We connect them to your data, training infra, and serving stack and kick off the first deployment.
Frequently Asked Questions
Common questions about hiring developers
What is the experience level of your ML engineers?
Our ML engineers typically have 4-8+ years across machine learning and software engineering. They're fluent in PyTorch or TensorFlow, comfortable with distributed training and model optimization, and have deployed models to production with proper serving, monitoring, and retraining pipelines.
How quickly can an ML engineer start on my project?
We can have an ML engineer onboarded within 48 hours of selection. For urgent deployments or production incidents, we can often expedite to 24 hours once access to your infrastructure is in place.
What if the ML engineer isn't a good fit?
We offer a 1-week risk-free trial. If the work or fit isn't right, we'll replace the engineer at no cost or provide a full refund. After the trial, replacements come with 1-week notice and handover of model and pipeline ownership.
Do your ML engineers work in my timezone?
We ensure at least 4 hours of overlap with your working hours for model reviews and deployment coordination. Many of our engineers adjust their schedules to maximize overlap with US or EU teams.
How do you ensure models stay reliable in production?
Our engineers set up experiment tracking, version models in a registry, keep features consistent between training and serving, and add drift and performance monitoring with clear rollback paths. A regressing model gets caught by metrics, not by users.
Can I scale into a full ML team?
Yes. We can pair ML engineers with data scientists, data engineers, and MLOps specialists to cover the full lifecycle from data pipeline to served model. Teams scale from 2 to 10+ members as your ML ambitions grow.
Ready to Hire Machine Learning Engineers?
Get matched with expert ML engineers in 24 hours. Start shipping models to production in 48 hours.
Or email contact@hevcode.com.