Hire machine learning engineers in India — production ML and MLOps specialists
Full-lifecycle ML engineers from India's unicorn and GCC ML teams. From training to production monitoring.
Machine learning engineers who can take a model from research to production — training pipelines, serving infrastructure, monitoring, and retraining — are among the most valuable and scarce engineering profiles globally. India's ML engineering community, trained in the production ML environments of India's hyperscale consumer internet companies and GCC ML labs, provides this full-lifecycle capability at 65–70% lower cost than US equivalents.
Why companies hire dedicated Machine Learning Engineers.
The research-to-production gap is the critical bottleneck
Data science research produces prototype models. ML engineers build the infrastructure that turns those prototypes into reliable, monitored, production systems.
MLOps is increasingly mandatory
Model drift, data distribution shifts, and inference cost management require dedicated MLOps infrastructure. ML engineers who own this prevent production model degradation.
ML systems compound in value
Well-architected ML infrastructure — feature stores, experiment tracking, model registries, A/B frameworks — enables faster model iteration and compounding improvement.
Why hire Machine Learning Engineers from India.
World's largest engineering talent pipeline
India produces 1.5M+ engineering graduates annually across IITs, NITs, IIITs, and hundreds of accredited colleges — the deepest single-country technical talent pool globally.
English as the professional language
English is the default language of India's technology sector — code, documentation, architecture discussions, and business communication are all conducted in professional English.
4–8 hours of live US time-zone overlap
IST (UTC+5:30) provides genuine synchronous collaboration windows for standups, code reviews, and design sessions with US and EU teams.
GCC and startup ecosystem training
India's 1,800+ Global Capability Centres and unicorn ecosystem (Razorpay, Meesho, CRED, Swiggy) have trained engineers in product-grade software development at global scale.
65–72% cost savings vs US market
Fully-loaded offshore cost through Remvix runs 28–35% of US-equivalent compensation — without sacrificing seniority or code quality.
What to look for.
- PyTorch, TensorFlow, JAX — training frameworks
- scikit-learn, XGBoost, LightGBM, CatBoost — classical ML
- MLflow, Weights & Biases, Neptune — experiment tracking
- Feast, Tecton — feature stores
- Kubeflow, Vertex AI Pipelines, SageMaker Pipelines — ML pipelines
- Triton Inference Server, BentoML, TorchServe — model serving
- Evidently, Arize, WhyLabs — model monitoring
- Docker, Kubernetes, Ray — distributed ML infrastructure
What they own.
- 01Build and maintain training pipelines for batch and online learning
- 02Implement feature engineering and feature store management
- 03Deploy models to production serving infrastructure
- 04Set up model monitoring for data drift and prediction drift
- 05Build A/B testing infrastructure for model evaluation
- 06Implement automated retraining pipelines
- 07Optimise model inference latency and serving cost
- 08Collaborate with data scientists to productionise model research
What to know before you start.
Production ML engineering vs research
Many ML engineers have training experience but limited serving, monitoring, and retraining experience. Remvix explicitly screens for full-lifecycle ML — not just model training.
MLOps tooling maturity
The MLOps tooling ecosystem evolves rapidly. Remvix screens for the specific tools relevant to your ML platform — Kubeflow, Vertex AI, SageMaker, or custom infrastructure.
Classical ML vs deep learning
Many ML problems are better solved with XGBoost or LightGBM than with neural networks. Engineers who default to deep learning for all problems may not be the best fit. Remvix screens for pragmatic model selection.
Work with us the way that fits.
Dedicated Team
A full-time developer or team working exclusively for your company — embedded in your sprints, tools, and culture.
Offshore Staffing
Individual dedicated hires at 65–72% lower cost than US equivalents. No minimum headcount.
Permanent Hiring
Full-time permanent hires with Indian-market competitive compensation and multi-year retention infrastructure.
Contract Staffing
Short-term or project-based engagements — surge capacity, pilots, or trial-before-permanent.
Employer of Record (EOR)
No Indian entity required. Remvix employs the team; you direct the work and pay one monthly invoice.
Which industries hire Machine Learning Engineers from India.
How we hire and operate your team.
Pre-screened network, not cold sourcing
Remvix maintains a continuously updated pre-screened network of candidates per role category. Shortlists are delivered within 7 days because sourcing starts before you ask.
Technical screening calibrated to your bar
We don't use generic assessments. Live coding, system design walkthroughs, and written communication reviews are all calibrated to your specific stack, seniority, and team norms.
You make every hire decision
Remvix provides pre-qualified shortlists. Your team runs the technical interviews and makes every final hire decision. We remove noise; you set the bar.
Enterprise operating infrastructure from day one
Payroll, statutory compliance, health benefits, equipment, IP assignment, and HR business partner support are all included. Your hire is fully operational within 3 weeks of kickoff.
Retention as an operating commitment
Competitive Indian-market compensation, L&D access, career pathing, and HR support drive 18–36 month average tenures — not hiring-agency churn.
- Payroll & tax filing
- Statutory compliance
- Health benefits
- Laptop & secure device
- IP assignment
- HR business partner
- 7-day shortlists
- 60-day replacement guarantee
Common questions.
Do ML engineers know PyTorch and TensorFlow?+
Yes — PyTorch is dominant in India's ML community. TensorFlow and JAX are also available. We screen for the framework relevant to your ML codebase.
Can they build production model serving infrastructure?+
Yes — Triton Inference Server, BentoML, TorchServe, and custom FastAPI serving wrappers are all in scope for ML engineers with production serving experience.
Can they implement model monitoring and drift detection?+
Yes — data drift, prediction drift, and model performance monitoring with Evidently, Arize, or WhyLabs are screened for MLOps-responsible ML engineer roles.
Do they know feature stores?+
Yes — Feast and Tecton feature store implementation is available for engineers at companies with mature ML platforms.
How much does a senior ML engineer cost through Remvix?+
Approximately $78–95K all-in annually. US equivalent is $270–340K total compensation.
Can ML engineers build distributed training pipelines?+
Yes — distributed training with PyTorch DDP, Ray Train, and GPU cluster management is available from engineers with large model training experience.
Do they know Kubeflow or SageMaker ML pipelines?+
Yes — Kubeflow Pipelines, Vertex AI Pipelines, SageMaker Pipelines, and Airflow-based ML pipelines are all available depending on your ML platform.
Can they implement A/B testing frameworks for model evaluation?+
Yes — online A/B testing infrastructure for model evaluation, traffic splitting, and metric collection is a standard MLOps engineering responsibility.
Can they work on classical ML problems as well as deep learning?+
Yes — XGBoost, LightGBM, and CatBoost for tabular data problems are standard competencies alongside deep learning frameworks.
How quickly can an ML engineer contribute?+
Most senior ML engineers are making meaningful contributions to the ML pipeline within 2–3 weeks of onboarding.
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