Project Overview
The client, a fast-moving AI startup, needed more than just extra developers. They needed a dedicated engineering hub capable of running rapid experiments, prototyping cutting-edge AI models, and pivoting quickly as their product hypotheses evolved. The goal was to scale a 25-member team in India, one built specifically for innovation velocity, with the flexibility to shift focus areas as the startup's roadmap demanded.
Challenges We Addressed
- Experimentation at Speed: The startup required constant testing of new AI approaches, demanding engineers who could prototype, iterate, and discard ideas without friction.
- Specialized Talent Scarcity: Finding engineers with hands-on experience in ML, LLMs, and AI infrastructure in their home market was slow and prohibitively expensive.
- Flexibility vs. Stability: The team needed to be stable enough to retain knowledge but flexible enough to pivot workstreams based on weekly product decisions.
- Knowledge Continuity: Rapid experimentation risked losing institutional knowledge, requiring a structured hub model to document and preserve learnings across iterations.
Project Goals
- Specialized Talent at Scale: Hire 25 engineers with deep expertise in LLMs, MLOps, and AI infrastructure within a defined timeline and budget.
- Experimentation Velocity: Enable rapid prototyping and experimentation cycles across ML and AI product areas with minimal friction.
- Flexible Team Structure: Build a hub model that adapts to shifting product priorities without losing team momentum or stability.
- Knowledge Management: Establish documentation and knowledge retention practices to preserve learnings across all experimentation cycles.
Our Approach
- We worked closely with the client's founding team to define the exact AI and ML skill profiles needed across research, engineering, and infrastructure roles.
- We sourced and hired 25 engineers with deep expertise in LLMs, MLOps, computer vision, and AI-native backend development.
- We designed a sprint structure built for rapid prototyping with short cycles, fast feedback, and defined criteria for continuing or killing experiments.
- We implemented documentation protocols and internal wikis to ensure every experiment, outcome, and learning was captured and accessible.
GCC Highlights
- 25-Member AI Hub Covering LLMs, MLOps, Computer Vision, and Infrastructure Roles
- Purpose-Built Sprint Model Designed to Run Multiple AI Experiments Every Month
- All Members Hired for AI-Native Skill Sets Unavailable at This Scale Onshore
- Flexible Hub Structure Built to Pivot Workstreams and Refocus Within Days
- Structured Documentation Practices in Place to Retain All Experimental Learnings
Project Results
The NeuralNest engineering hub was fully operational with all 25 members contributing to live AI experiments and product development within the agreed timeline. The client's experimentation velocity increased dramatically, with the hub running more AI model iterations in the first quarter than their core team had managed in the previous year. Access to specialized AI talent at scale and at a fraction of onshore cost gave the startup a meaningful competitive edge during a critical growth phase. The flexible hub model continues to adapt as the product roadmap evolves.