Hire ML Engineers
Hire Machine Learning Engineers — Matched in 48 Hours
Your ML roadmap shouldn't stall because you can't find the right engineer. Kovil AI places elite machine learning engineers — screened for production ML, MLOps, and real business impact.
What You Get With a Kovil AI ML Engineer
Every engagement includes managed delivery, milestone oversight, and our 2-week risk-free trial.
ML engineers with production experience — models that run in the real world, not just notebooks
End-to-end ML pipeline expertise from data ingestion through model deployment and monitoring
MLOps specialists who set up proper versioning, retraining pipelines, and drift detection
Experience across computer vision, NLP, recommendation systems, and time-series forecasting
2-week risk-free trial — if the fit isn't right, we rematch at zero cost
From Brief to Shipping in 14 Days
Our process is fast, structured, and risk-free. Learn more at how it works.
Describe Your Needs
Fill a brief intake form. A Delivery Lead contacts you within 24 hours to scope your requirements, tech stack, and timeline.
Meet Your Expert
We match you with a vetted ML Engineer in 24–48 hours. Review their profile, join an intro call, and start your 2-week risk-free trial.
Watch Results Roll In
Your first feature ships within 14 days. An Engagement Manager audits every commit. Scale up or down with zero lock-in.
Skills & Technologies
Our ML Engineers are vetted across these tools and platforms.
Proven Results
FinTech / Lending
AI Automation Transforms Deal Processing for Digital Lending Platform
Faster Deal Processing Turnaround
Reduced Manual Underwriting Effort
Frequently Asked Questions
What is the difference between an ML engineer and a data scientist?
A data scientist focuses on analysis, modeling, and experimentation. An ML engineer focuses on taking those models to production — scalable pipelines, real-time serving, monitoring, and reliability. Kovil AI has both.
What ML frameworks do your engineers specialize in?
Our engineers are proficient across PyTorch, TensorFlow, Scikit-learn, XGBoost, and LightGBM. On the MLOps side: MLflow, Kubeflow, AWS SageMaker, Azure ML, and Vertex AI.
Can you build an end-to-end ML pipeline from scratch?
Yes. From data ingestion and feature engineering through training, evaluation, deployment, and monitoring. We design for reliability and retrainability from day one.
How do you handle model drift and retraining?
We build retraining pipelines and drift detection into every production ML system — automated alerts when model performance degrades, scheduled or triggered retraining, and A/B testing for new model versions.
How quickly can I get an ML engineer started?
Matched in 24–48 hours, onboarded within a week, delivering first results in 14 days.
Do you offer fixed-price ML project delivery?
Yes. We scope, build, and ship ML projects at a fixed price with milestone-gated delivery. You approve each phase before we move forward.
Start Your 2-Week Risk-Free Trial
Fixed price. Milestone-gated. Zero delivery risk. Zero termination fees.