The AI & GenAI Engineer Hiring Playbook for Early-Stage Founders
Almost every software resume in India now claims 'GenAI experience'. Here is how high-conviction founders distinguish applied model builders from surface-level API wrapper developers.
1. The Three AI Archetypes in the Indian Market
Has called OpenAI or Anthropic endpoints via LangChain. Good for quick front-end prototypes, but struggles with evaluation harnesses, latency optimization, and cost modeling.
Expert in fine-tuning, RAG architecture, vector indexing, synthetic data generation, and guardrails. Understands deterministic vs non-deterministic production systems.
Deep mathematical background in training architectures, PyTorch, and CUDA optimization. Crucial for foundational model training, but often slower for product-focused shipping.
2. 4 Practical Screening Signals
- Evaluation Frameworks: Can they explain how they benchmark model hallucinations and regressions in production without relying on human eye tests?
- Latency & Token Economics: Do they optimize context windows, caching, and small language model (SLM) routing to keep unit costs manageable?
- Systems Engineering Fundamentals: Strong GenAI engineers are first and foremost great software engineers who understand concurrency, queues, and distributed state.
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