Hire Top Machine Learning Engineers in India!
Years of experience
Customer satisfaction
What Makes Benchkart a Great Partner for Hiring Machine Learning Engineers?
Benchkart blends AI-driven talent intelligence, a deep vendor ecosystem, and Avance Group’s governance to deliver high-performance ML engineering with unmatched speed and reliability.
Security-First Delivery
Secure model deployment, governed data workflows, compliance alignment, and enterprise-grade delivery.
Proven Talent Network
ML engineers sourced from our bench, passive talent pipelines, and 2,000+ vetted partners.
Growth-Driven Engineering
Experts in scalable ML systems, feature engineering, MLOps automation, and measurable business impact.
Cutting-Edge Tech Stack
Developers bring hands-on expertise in:
Proficiency in Python
TensorFlow
PyTorch
Scikit-learn
MLflow
Airflow
Databricks
Hugging Face
AWS/Azure/GCP ML stacks, and modern MLOps frameworks
Services We Offer
End-to-End ML Model Development
Production ML & MLOps Engineering
Deep Learning Solutions
Predictive Analytics & Forecasting
Cloud ML Platform Development
Model Optimization & Performance Tuning
Expertise of Our Machine Learning Engineers
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Supervised & unsupervised learning
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Time-series, clustering, regression models
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Model evaluation & diagnostics
Roles & Responsibilities: Machine Learning Engineers design algorithms, perform data exploration, engineer features, train models, validate performance, and deliver optimized ML components aligned to business objectives.
TensorFlow, PyTorch
CNNs, RNNs, Transformers
Embedding models, attention mechanisms
Roles & Responsibilities: They build and optimize deep learning architectures for NLP, computer vision, and sequence-based tasks, ensuring high-performance models ready for production environments.
MLflow & Kubeflow
Airflow, Prefect, Dagster orchestrations
Containerized inference (Docker, Kubernetes)
Roles & Responsibilities: Engineers implement automated ML pipelines, deploy models to scalable endpoints, manage model lifecycle/versioning, and monitor drift, performance, and reliability in production.
AWS SageMaker
Azure Machine Learning
GCP Vertex AI
Roles & Responsibilities: They leverage managed ML platforms for training, tuning, hosting, and workflow orchestration; optimize cloud compute usage; and ensure compliance with enterprise cloud standards.
Feature stores (Feast, Databricks FS)
Data pipelines & ingestion frameworks
Feature versioning & reproducibility
Roles & Responsibilities: ML engineers work closely with data engineering teams to create feature pipelines, manage datasets, ensure reproducibility of experiments, and maintain efficient input pipelines for training and inference.
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Hyperparameter tuning
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Model compression & quantization
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SHAP/LIME explainability
Roles & Responsibilities: They optimize model performance through tuning and compression, validate models through rigorous testing, implement explainability frameworks, and translate model behavior into business-friendly insights.
How We Hire Developers
With a structured multi-stage hiring process, we onboard only high-calibre Machine Learning Engineers.
Skill Benchmarking
Thorough CV & background evaluation
Human Vetting
Interview with HR specialist
Experience Validation
Communication & soft-skills assessment
Cultural Fit
Technical interview with Senior ML Architect
Hire Developers from Benchkart
STEP 1 Inquiry
We understand your data, domain needs, ML use cases, platform preferences, and deployment targets.
STEP 2 Developer Selection
AI-matched ML profiles curated from our bench, passive ML specialists, and vetted vendor network.
STEP 3 Integration
Engineers join your workflows, repos, cloud environments, and sprint cycles seamlessly.
STEP 4 Scaling
Scale your ML team confidently with SLA-governed delivery and continuous talent availability.
Choose the Right Development Model for Your Business
Flexible models designed for AI and ML product development.
ML Team Augmentation
Quickly add Machine Learning Engineers to accelerate model development and experimentation.
Dedicated ML & AI Squad
A full-time ML team aligned exclusively with your AI roadmap, experiments, and deployment cycles.
Full ML Development Outsourcing
We handle research → modeling → feature engineering → deployment → monitoring end-to-end.
Top Reasons to Choose Benchkart for Machine Learning Engineer Hiring
Quality + speed + governance built for enterprise delivery.
Built on Avance Group’s Talent Engine
AI-driven ML talent identification backed by strong governance and delivery oversight.
Unmatched Speed 48-Hour Shortlists
Receive curated ML Engineer profiles within 48–72 hours.
Massive Vendor Ecosystem 2000+ Strong
Access India’s deepest network for applied AI, MLOps, and ML product engineering talent.
Wisestep ATS + CRM Skill-First Precision
AI ranks ML engineers based on modeling depth, cloud platform expertise, deployment maturity, and domain alignment.
Bench-Ready ML Engineers
Experts in ML, deep learning, optimization, MLOps automation, feature stores, and cloud-native ML workflows.
Governed Delivery with Enterprise SLAs
Quality gates, performance benchmarks, model governance, drift monitoring, and continuity frameworks.
Backed by Avance Group Global Trust
Operating across 14+ countries with long-standing experience in delivering AI and data engineering programs.
Need a Dedicated Machine Learning Team?
Hire pre-vetted Machine Learning Engineers who deliver accurate, scalable, and production-ready ML solutions from day one.
Shortlist in 48 hours. Onboarding in 5–10 days.
Operates across
Industries We Support for Machine Learning Engineer Hiring
Benchkart supports ML-driven transformation across global industries.
BFSI & FinTech
Healthcare
E-commerce
Manufacturing
Logistics
Telecom
Hospitality
FAQs
1. What does a Machine Learning Engineer do?
They design, train, deploy, and monitor ML models, integrating them into production systems and ensuring ongoing performance and reliability.
2. What skills should a Machine Learning Engineer have?
Python, TensorFlow/PyTorch, ML algorithms, data engineering basics, MLOps tooling, cloud ML platforms, and strong mathematical foundations.
3. What is the cost to hire an ML Engineer in India?
Typically $30–$70 per hour, depending on ML depth, domain experience, and deployment capabilities.
4. Do ML Engineers work on production deployments?
Yes, including APIs, batch pipelines, real-time inference, and CI/CD integration.
5. Can Benchkart deliver ML Engineers within 48 hours?
Yes, curated shortlists are typically delivered within 48–72 hours.
6. Do ML Engineers handle both ML and MLOps tasks?
Many do, they build models and productionize them through automated pipelines and monitoring frameworks.
7. Can I hire a full ML team?
Yes, including ML engineers, data engineers, MLOps engineers, and data scientists.
8. Which cloud ML platforms do your engineers support?
AWS SageMaker, Azure ML, GCP Vertex AI, Databricks ML runtime.
9. Do ML Engineers collaborate with analysts and product teams?
Yes, they work with business stakeholders to define use cases and convert them into ML solutions.
10. Why hire ML Engineers from Benchkart?
You gain vetted specialists backed by AI-driven matching, vendor governance, enterprise SLAs, and delivery oversight.
Get in Touch with Benchkart Reliable Tech Talent Delivery
We’re happy to answer any questions you may have and help you understand how Benchkart can support your technology hiring and delivery needs.
Your benefits:
- Vendor-verified
- Delivery-focused
- AI-driven
- Results-oriented
- Execution-ready
- Transparent
What happens next?
We schedule a quick call at your convenience
We understand your role, timeline, and delivery context
We activate the right talent path