INDIA × AI — 2026 OPPORTUNITY REPORT
The biggest talent gap in tech history is opening right now — and the labs paying $2M+ salaries have set up shop in India.
Large number of AI Datacentres are built, but very less AI Researchers who can actually bring innovation to the table.
WHO IS HIRING IN INDIA
OpenAIOpened India office in 2024. Scaling global headcount to 8,000 by Dec 2026. Roles span research, policy, and engineering.
AnthropicEstablished India presence focused on safety research. Offers equity-heavy packages and direct access to frontier model training.
CohereEnterprise AI focused lab expanding in India. Targets NLP researchers and fine-tuning specialists for LLM infrastructure.
Google DeepMindBangalore and Hyderabad hubs actively recruiting. Roles in Gemini, robotics, and fundamental ML research.
"The easy SaaS wave is over;
The next phase of wealth creation belongs to those with fundamental, systems-level AI expertise."
Merging American and Chinese AI models into one,
we created Rubin.
Strong Performance with Efficient Inference and Training,
outpacing Earlier Vanilla Transfomer model by 52%
A lightweight 500 million parameter model built for research and education. Ideal for learning the fundamentals of training, fine-tuning, and deployment, it provides fast iteration speed and serves as a perfect entry point for exploring transformer-based architectures without heavy compute requirements.
A medium-scale foundation model trained with pretraining, supervised fine-tuning (SFT), and GRPO (Generative Reinforcement with Preference Optimization). Designed for practical applications, it offers strong generalization while remaining efficient, making it a balanced choice for academic and applied research.
A large-scale model featuring Latent Head Attention, enabling improved representation learning and context management. This scaled version is optimized for both reasoning and generation tasks, delivering robust performance in complex workloads while remaining manageable on modern GPU clusters.
Our flagship model built with a Mixture of Experts (MoE) architecture combined with the Deepseek MLA and DSA. leading to more coherent, efficient, and explainable outputs. It scales to enterprise-grade workloads, supporting advanced research, production deployments, and cutting-edge applications.
BRAINOID LABS — MODEL SUITE
Full-stack open models from 500M to 24B — training code, inference code, and weights released for the research community.
| Specs | Rubin 500MRESEARCH | Rubin 2BPRODUCTION | Rubin 8BPRODUCTION | Rubin 24BPRODUCTION |
|---|---|---|---|---|
| Parameters | 500M | 2B | 8B | 24B |
| Training Tokens | 2GB | 800B | 1.6T | 2.7T |
| Training Code | ✓ | ✓ | ✓ | ✓ |
| Inference Code | ✓ | ✓ | ✓ | ✓ |
| Inference Mode | KV Cache | KV Cache + Sink | KV Cache | KV Cache |
| Optimizer | muon with adamw | muon with adamw | muon with adamw | custom muon with adamw |
| LR Scheduler | trapezoidal with cosine annealing | trapezoidal | warmup with cosine annealing | cosine annealing |
| Attention | Grouped Query Attention | Grouped Query Attention | Grouped Query Attention with LHA | Sliding Window with MoE & LHA |
| Positional Encoding | Standard ROPE | YaRN ROPE | YaRN ROPE | YaRN ROPE |
| Normalisation | RMS | RMS | RMS | RMS |
| Model Weights | available | available | to be released soon | to be released soon |
| GPU Used | 1 × H100 SXM | 8 × H200 SXM | 4 × B200 | 32 × B200 |
| End-to-End Training | ✓ | ✓ | ✓ | ✓ |
| Purpose | Research | Production | Production | Production |
| Local Inference | ✓ | ✓ | — | — |
| Model Type | base model | base model + SFT + GRPO | instruction model [SFT] | to be released soon |
* Model weights for 8B and 24B variants will be released in upcoming checkpoints.
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