Hire Machine Learning Engineers
in India
Senior ML engineers ready in 48 hours. Build recommendation systems, fraud detection models, and production-grade ML pipelines with PyTorch, TensorFlow, and MLflow — at 60% less than local rates.
What Our ML Engineers Build for You
From model development to production MLOps pipelines
ML Model Development
Build and train classification, regression, ranking, and clustering models — with rigorous cross-validation, hyperparameter tuning, and interpretability.
Recommendation Systems
Collaborative filtering, content-based, and hybrid recommendation engines — from item-based similarity to deep learning recommenders.
Fraud & Anomaly Detection
Transaction fraud models, anomaly detection for operations, and behaviour-based risk scoring with real-time inference APIs.
Predictive Analytics
Business forecasting models — demand prediction, churn prediction, customer lifetime value, and revenue forecasting.
Deep Learning Models
Convolutional, recurrent, and transformer architectures built with PyTorch or TensorFlow for image, sequence, and text tasks.
MLOps & Pipelines
Version-controlled training pipelines, model registry (MLflow), automated retraining triggers, and production monitoring.
Feature Engineering
Build feature stores, data preprocessing pipelines, and automated feature selection for clean, reliable model inputs.
Model Evaluation & A/B Testing
Offline evaluation frameworks, online A/B testing infrastructure, and shadow deployment to safely roll out new models.
Cloud ML Deployment
Deploy models on AWS SageMaker, Vertex AI, or Azure ML — with autoscaling endpoints, latency budgets, and cost monitoring.
ML Tools & Technologies We Cover
Why Hire ML Engineers Through TechTeamsOnline?
Production ML Experience
Our ML engineers have shipped models to production with monitoring, retraining automation, and drift detection — not just Jupyter notebooks.
48-Hour Matching
Share your ML requirements. Receive 2–3 pre-vetted ML engineer profiles in 48 hours, ready for your technical interview.
7-Day Risk-Free Trial
Work with your ML engineer for a full week. Not the right fit? You pay nothing.
60% Cost Savings
Hire senior ML engineers at $2,000–$5,500/month — compared to $130,000–$220,000/year in the US.
MLOps-First Mindset
Our ML engineers build maintainable, monitored, reproducible systems — not one-off models that become black boxes.
Free Replacement Guarantee
If your ML engineer leaves or underperforms, we replace them within 7 business days at no cost.
How We Vet ML Engineers
Portfolio & CV Screen
We review production ML systems shipped, model types built, and MLOps tooling used.
ML Technical Assessment
Train a model on a real dataset, evaluate properly, explain the feature engineering and model choice.
ML Systems Interview
Design an end-to-end ML system: data pipeline, training, serving, monitoring, and retraining strategy.
Communication Fit
English proficiency and async collaboration style evaluated.
What Clients Say About Our ML Engineers
"Our ML engineer built a recommendation engine that increased our average order value by 22% in 8 weeks. Best investment we've made this year."
"The fraud detection model our ML engineer built reduced false positives by 60% while catching 15% more fraud. Excellent work."
"Our churn prediction model is now in production, retrains weekly, and alerts our sales team 30 days before customers are likely to leave."
Frequently Asked Questions
What types of ML problems can your engineers solve?
Our ML engineers solve classification, regression, clustering, ranking, recommendation, anomaly detection, forecasting, and NLP problems — selecting the right algorithm, building training pipelines, and deploying with monitoring.
Do your ML engineers have experience with production ML systems?
Yes. Our ML engineers have shipped models to production with feature stores, serving endpoints, A/B testing, drift monitoring, and automated retraining pipelines.
What ML frameworks do your engineers use?
scikit-learn for classical ML, PyTorch and TensorFlow for deep learning, MLflow and Kubeflow for MLOps, and AWS SageMaker, Vertex AI, or Azure ML for cloud training and serving.
How long does it take to build a recommendation system?
A basic collaborative filtering system takes 4–6 weeks. A production-grade system with online feature serving, A/B testing, and real-time personalisation typically takes 2–4 months.
Can your ML engineers build fraud detection models?
Yes. Our engineers build fraud detection systems using XGBoost, LightGBM, neural networks, and graph-based approaches — handling class imbalance and real-time scoring APIs.
What is MLOps and do your engineers practice it?
MLOps applies DevOps to ML — version-controlled data and models, automated training pipelines, model registry, and production monitoring. Our engineers implement MLOps from the start.
Ready to Hire a Senior ML Engineer?
Get 2–3 pre-vetted ML engineer profiles in 48 hours. Start with a 7-day risk-free trial.