Dedicated AI and ML teams from India — production-proven, IIT/IISc alumni
ML engineers, data scientists, and LLM specialists. Pre-vetted, full-time, at 35% of US cost.
Artificial intelligence and machine learning companies face the sharpest talent shortage in technology. The competition for ML engineers in the US concentrates hiring into a narrow band of overpriced, low-tenure candidates. India is home to world-class AI talent — IIT, IISc, and BITS alumni who have shipped production ML systems at Google DeepMind, Microsoft Research, Meta AI, and India's leading AI companies — available at a fraction of US market cost.
What makes hiring for AI & Machine Learning hard.
ML engineering talent is acutely scarce in Western markets
The global ML talent pool is thin, concentrated in a handful of cities, and competitively bid up by the largest tech companies. India's pipeline of IIT and IISc ML graduates is one of the few credible alternatives to US talent at production quality.
Research-to-production gap
Many AI companies struggle to translate research prototypes into production systems. India has a large pool of engineers who specialise in productionising ML — MLOps, serving infrastructure, and model monitoring — not just research.
LLM and generative AI skill gap
The LLM era has created a new skill set — RAG, fine-tuning, agent frameworks, evaluation pipelines — that is in short supply globally. India's AI talent has adapted quickly; Remvix has pre-screened LLM engineers available.
Data engineering for ML pipelines
Feature engineering, training data pipelines, and data quality systems require dedicated data engineers who understand ML workflows. This is a distinct skill from analytics engineering and is scarce in US markets.
Cost of building an AI team
A 5-person ML team in the US (3 ML engineers + 1 data engineer + 1 MLOps) costs $1.5–2M in loaded annual compensation. The same team through Remvix costs $450–600K — a 65–70% reduction.
Key roles and market dynamics.
AI and ML companies hire from India across the full ML lifecycle — research scientists, ML engineers, data engineers, and MLOps specialists. India's strongest contribution is in production ML engineering: systems that take models from research to deployed, monitored, production systems.
| Role | Demand | Notes |
|---|---|---|
| ML Engineer | Critical | PyTorch, TensorFlow, JAX — training, fine-tuning, and production deployment. |
| LLM Engineer | Critical | RAG, fine-tuning (LoRA/QLoRA), LangChain, LlamaIndex, evaluation pipelines. |
| ML Research Scientist | High | Novel model architectures, publications, IIT/IISc/BITS academic backgrounds. |
| Data Scientist | Very High | Exploratory analysis, feature engineering, A/B testing, statistical modelling. |
| Data Engineer (ML) | Very High | Feature stores, training data pipelines, Spark, dbt, data quality systems. |
| MLOps Engineer | High | Model serving, monitoring, drift detection, retraining pipelines, Kubeflow. |
| AI Product Engineer | High | LLM product features, latency optimisation, multi-modal systems. |
| Computer Vision Engineer | High | Object detection, segmentation, OCR — PyTorch vision, OpenCV. |
Services mapped to your industry needs.
AI & Data Talent
IIT/IISc alumni with production ML experience — ML engineers, data scientists, and LLM specialists pre-vetted on technical depth.
AI Engineering Recruitment
LLM, RAG, agent framework, and AI product engineers — production-proven, not just notebook scientists.
Machine Learning Recruitment
Full-lifecycle ML engineers: training, fine-tuning, deployment, monitoring, and retraining.
Data Engineering Recruitment
ML-aware data engineers who build the pipelines that feed your models.
Engineering Teams
Dedicated AI/ML pods — ML engineers + data engineers + MLOps — operating as your captive India R&D team.
Work with us the way that fits.
Contract
Short-term or project-based engagement — ideal for surge capacity, pilot programmes, or trial-before-permanent hiring.
Permanent
Dedicated full-time headcount with competitive Indian-market compensation, statutory benefits, and multi-year retention infrastructure.
Dedicated Team
A pod of 3–10 working exclusively for your company under a Remvix-managed team lead — the GCC model without the entity setup.
Managed Service
Remvix operates the team end-to-end: recruiting, payroll, performance management, compliance, and reporting.
Regulated industry requirements.
AI and ML companies must ensure model IP, training data, and proprietary architecture are protected. Remvix's standard IP assignment covers all model weights, training code, evaluation frameworks, and research outputs. Team members sign confidentiality clauses covering unpublished research, proprietary datasets, and model architectures before any work begins.
Real outcomes.
AI infrastructure company builds dedicated ML pod in 3 weeks
An AI infrastructure company needed to rapidly expand its ML engineering capacity for a product deadline. Remvix sourced 5 engineers with PyTorch and distributed training experience from IIT Bombay and IISc alumni networks.
See all case studiesCommon questions.
Do your ML engineers have production experience, not just research?+
We specifically screen for production ML experience — models deployed and serving real traffic — not just Jupyter notebooks or Kaggle competition scores.
Can you recruit LLM engineers for RAG and fine-tuning?+
Yes — RAG pipeline engineers, fine-tuning specialists (LoRA, QLoRA, RLHF), and LLM evaluation engineers are available from India's growing LLM talent pool.
What frameworks do your ML engineers know?+
PyTorch is the most common, followed by TensorFlow, JAX, and Hugging Face. We recruit for your specific framework requirements.
Can you recruit ML research scientists with publication records?+
Yes — we source from IIT, IISc, and BITS alumni with NeurIPS, ICML, and ICLR publications. Research scientist searches typically take 14–21 days.
Who owns the model IP and training data?+
Your company owns all model weights, training code, evaluation frameworks, and associated IP. This is documented in IP assignment clauses signed before any engineer begins.
Can ML engineers work in US hours for synchronous collaboration?+
Yes — many ML engineers are available for US morning-aligned schedules (12:00–20:00 IST = 6:30–14:30 EST). Async collaboration works well for the rest of the day.
Can you build a full ML team — not just one engineer?+
Yes — AI/ML pods (ML engineers + data engineers + MLOps) are one of our most common pod configurations. Teams range from 3 to 15 members.
How long does recruiting a senior ML engineer take?+
7–10 days for a shortlist of 3–5 candidates; onboarding within 3 weeks. Highly specialised roles (computer vision, NLP research) may take 14–21 days.
Are your AI engineers familiar with cloud ML platforms?+
Yes — AWS SageMaker, GCP Vertex AI, and Azure ML are common. Databricks and Hugging Face Inference Endpoints are also well-represented.
Can we hire offshore for a new AI product line we're launching?+
Yes — launching a new AI product line is one of the most common BOT (Build-Operate-Transfer) use cases. Remvix builds the team immediately on its infrastructure; you transfer to your entity when ready.
Explore adjacent sectors.
Engineering, revenue, and CX talent from India. Dedicated, full-time, at 30% of US cost.
Pre-vetted FinTech talent from India: engineers, analysts, and ops specialists.
Engineering, clinical operations, and CX from India. Compliant from day one.
Build your team.
Your next great hire is in India. We'll find them.
Talk to a Remvix specialist about your roles, timeline, and budget. Get a tailored shortlist within 7 days.