Hire Machine Learning Developers
Build predictive models, recommendation systems, and MLOps pipelines that scale. Our ML engineers deliver measurable accuracy improvements and production-ready intelligent systems.
- Role
- Machine Learning Developers
- Stack
- PythonPyTorchTensorFlowHugging FaceApache Spark
- Overlap
- US and EU/UK business hours
- Availability
- 5-7 business days
- Model
- Dedicated, Hourly, Project-Based
Why Hire Our Machine Learning Developers?
Expert ML engineers who build, deploy, and monitor models that create real business impact.
Research-Backed Engineering
Our ML engineers apply state-of-the-art research to build models that are accurate, efficient, and production-ready.
End-to-End MLOps
From data preprocessing to model deployment, monitoring, and retraining pipelines — we handle the full ML lifecycle.
Domain-Specific Models
We train and fine-tune models for healthcare, finance, NLP, computer vision, and recommendation systems.
Scalable Deployment
Serve models at scale with FastAPI, TorchServe, TensorFlow Serving, and cloud-native infrastructure on AWS or GCP.
Our Development Process
- 01
Problem framing & data audit
Define the ML objective, assess data quality, and validate feasibility.
- 02
Data collection & engineering
Build feature engineering pipelines and curate training datasets.
- 03
Engineer selection
Match ML engineers with relevant domain and framework expertise.
- 04
Model training & evaluation
Experiment, train, tune hyperparameters, and benchmark against baselines.
- 05
MLOps & deployment
Set up CI/CD for models, versioning, serving, and automated retraining.
- 06
Monitoring & iteration
Track model drift, performance degradation, and continuously improve accuracy.
Why CognyX AI?
- Certified & Senior Developers
- Clean & Scalable Code
- Fast Project Kickoff
- Dedicated Team Model
- Transparent Communication
- On-Time Delivery
- Performance-Optimized Apps
- NDA & IP Protection
- Flexible Engagement Models
- Ongoing Support & Maintenance
Our Core AI Services
- FinTech & Banking
- Healthcare & Pharma
- E-Commerce & Retail
- AdTech & Media
- Manufacturing & IoT
- SaaS & Software
- Insurance & Risk
- Logistics & Supply Chain
Industries we serve
Education
EdTech
Finance
Logistics
Supply Chain
Manufacturing
Retail
eCommerce
Hospitality
Travel
Insurance
Real Estate
Telecom
Predictive Analytics
- Demand forecasting
- Churn prediction models
- Fraud detection systems
- Risk scoring pipelines
- Time series forecasting
- Anomaly detection
Model Development & MLOps
- Custom model training (PyTorch/TF)
- Transfer learning & fine-tuning
- Feature store design
- MLflow & experiment tracking
- Automated retraining pipelines
- Model versioning & registry
ML System Integration
- REST API model serving
- Real-time inference pipelines
- Batch scoring at scale
- A/B testing frameworks
- Shadow mode deployment
- Edge ML & on-device inference
EXPERTISE IN MODERN TECH STACKS
- Python
- PyTorch
- TensorFlow
- Hugging Face
- OpenAI
- Gemini
- Apache Spark
- FastAPI
- Streamlit
- PostgreSQL
- AWS
- Google Cloud
- Docker
- Kubernetes
Flexible Hiring Models
Dedicated Developer
Full-time exclusive focus.
Hourly Hiring
Short-term tasks & consulting.
Project-Based
Fixed scope and timeline.
CognyX has been a fantastic partner in our AI-powered product development. They took us from ChatGPT and Manychat to a bespoke website that delivered what chat alone could not.
Dave Wolding, TripFoxFrequently Asked Questions
Everything you need to know before hiring our machine learning developers.
What ML frameworks do your developers use?
Our ML engineers work primarily with PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, XGBoost, and LightGBM — choosing the best tool for each problem.
Can you help us build ML models from scratch?
Yes. From problem framing and data collection through to model training, evaluation, and deployment, we handle the complete ML workflow.
Do you set up MLOps pipelines?
Absolutely. We implement end-to-end MLOps with experiment tracking (MLflow), model registries, CI/CD for models, and automated retraining.
How do you handle small or imbalanced datasets?
We apply techniques like SMOTE, data augmentation, transfer learning, active learning, and synthetic data generation to address data limitations.
Can you deploy models to production at scale?
Yes. We serve models using FastAPI, TorchServe, TensorFlow Serving, or SageMaker endpoints, with autoscaling and load balancing configured.
How do you monitor model performance after deployment?
We set up data drift detection, performance dashboards, automated alerts, and scheduled retraining pipelines to maintain model accuracy over time.
Tell us what you want to automate.
Free 30-min AI architecture review.
- [email protected]
- Phone
- +91 99748 12447
- Chat on WhatsApp