Hire ML Engineers
Hire senior ML engineers who build and deploy production-ready machine learning models with TensorFlow, PyTorch, and MLOps. They seamlessly integrate with your team or work in managed Pods—developing models, deploying at scale, and shipping ML solutions that drive results.
Hire Experts, Who Ship, Not Just Code
Get dedicated ML engineers with proven expertise in building and deploying production ML systems
Hire skilled ML engineers from Zeksta to build powerful machine learning solutions. Our ML engineers are experts in TensorFlow, PyTorch, scikit-learn, MLOps, model deployment, and production ML systems. They follow best practices, write clean code, and deliver high-performance ML solutions that meet your business objectives. Whether you need ML engineers, MLOps specialists, model deployment engineers, or production ML engineers, we have the right talent for your project.
Who you can hire from Zeksta
ML Engineering Experts
MLOps Specialists
Model Deployment Engineers
Production ML Engineers
ML Infrastructure Engineers
ML Consultants
Why hire ML engineers from Zeksta
Pre-Vetted Specialists
Only 2% of applicants pass our rigorous screening process. We conduct live coding sessions, ML system design challenges, model deployment exercises, and communication assessments to ensure you get only the best ML engineers.
Production ML Expertise
Not just model training. Our engineers are experts in deploying ML models at scale, MLOps, model monitoring, A/B testing, and production ML systems. They bridge the gap between research and production.
Fast Onboarding
Start interviewing candidates within days, not weeks. Our pre-vetted talent pool means you skip months of recruitment hassles and get productive ML engineers on your team faster.
Risk-Free Trial
Every hire comes with a 2-week trial period. If it's not working out, we replace the engineer at no extra cost. Your satisfaction is our guarantee—we're committed to finding the perfect fit for your team.
AI-Native Engineers
Our engineers use AI tools daily (Cursor, GitHub Copilot) to accelerate development. They understand how to leverage AI for ML engineering tasks while maintaining code quality and best practices.
Outcome-Oriented
Not just researchers—engineers who deploy models to production, optimize performance, ensure reliability, and genuinely care about delivering ML solutions that drive business results.
Our ML Engineering Tech Stack
Our ML engineers are proficient in the latest ML tools, frameworks, and best practices to deliver exceptional production ML systems.
ML Frameworks
- •TensorFlow
- •PyTorch
- •scikit-learn
- •XGBoost & LightGBM
- •Hugging Face
MLOps & Deployment
- •MLflow
- •Kubeflow
- •Model Serving (TensorFlow Serving, TorchServe)
- •A/B Testing & Experimentation
- •Model Monitoring & Observability
Cloud ML Platforms
- •AWS SageMaker
- •Google Vertex AI
- •Azure ML
- •Databricks ML
- •ML Infrastructure
Model Development
- •Feature Engineering
- •Model Training & Tuning
- •Model Evaluation & Validation
- •Hyperparameter Optimization
- •Model Versioning
Production Systems
- •Model Deployment
- •Kubernetes & Docker
- •API Development
- •Scalable ML Systems
- •Performance Optimization
Programming & Tools
- •Python
- •SQL
- •CI/CD for ML
- •Data Processing (Pandas, NumPy)
- •Jupyter & Notebooks
How to Hire ML Engineers
Hire ML Engineers in Weeks, Not Months. Our streamlined hiring process gets pre-vetted ML engineers on your team fast. Skip the lengthy recruitment cycles.
Share Your Requirements
Tell us about your ML project, model types, deployment requirements, MLOps needs, and the experience level you need. We'll help you define the right ML engineer profile.
Get Matched Profiles
We present 2-3 pre-vetted ML engineers who match your requirements. Review their experience, portfolio projects, and our assessment notes.
Interview & Select
Interview candidates directly with technical questions relevant to your ML stack and deployment needs. We can facilitate or let you run it entirely. You decide.
Risk-Free Trial
Start with a 2-week trial. The engineer joins your team, attends standups, and delivers real work. If not a fit, we replace at no cost.
Scale as Needed
Add more ML engineers or transition to a full ML team with data scientists, ML engineers, and MLOps specialists. We scale with your needs.
Working with Zeksta
| Zeksta | Hiring In-house | Virtual Platform | |
|---|---|---|---|
| Time to build a Team | 0-2 weeks | 3-6 months | 2-6 months |
| Cost of Recruiting | None | Very High | None |
| Guarantee of Success | |||
| Pre-Screened Talent | |||
| Termination Cost | None | Very high | None |
| Overall Cost Effectiveness | Very High | Low | Medium |
Time to build a Team
Cost of Recruiting
Guarantee of Success
Pre-Screened Talent
Termination Cost
Overall Cost Effectiveness
Ready to Hire ML Engineers?
Tell us about your ML project and requirements. We'll present pre-vetted candidates within days, and you can start with a risk-free trial.
Frequently AskedQuestions
We are trying to resolve your doubts before commencing with us. If you are still left with doubts, feel free to contact us.
Our ML engineers have extensive experience working with machine learning models, model deployment, MLOps, production ML systems, and ML infrastructure. They have worked on projects ranging from proof-of-concepts to production ML systems serving millions of users. You can review their portfolios and conduct interviews to ensure they meet your specific requirements.
Yes! We have ML engineers who specialize in model training, model deployment, MLOps, production ML systems, and end-to-end ML pipelines. Our engineers are experienced in various ML domains and can work on diverse project types from research to production.
Our ML engineers are proficient in Python, TensorFlow, PyTorch, scikit-learn, MLflow, Kubernetes, Docker, AWS SageMaker, Google Vertex AI, Azure ML, and modern ML tools. They also work with model serving, monitoring, A/B testing, and ML infrastructure.
We maintain high code quality through code reviews, best practices compliance, comprehensive testing, model evaluation, and following ML engineering best practices. Our engineers write clean, maintainable, and well-documented code optimized for production deployment.
Absolutely! You can start with one or two ML engineers and scale up as your project grows. We can quickly onboard additional ML engineers to your team, ensuring seamless collaboration and maintaining project momentum.
We offer flexible engagement models including full-time dedicated ML engineers, part-time engineers, or project-based contracts. You can choose the model that best fits your project timeline and budget requirements.
Yes, we can align our ML engineers' working hours with your time zone to ensure seamless communication and collaboration. We have engineers across different time zones to provide round-the-clock coverage if needed.
Still have questions?
Contact Us