How to Hire AI Engineers in India: What to Look For in 2026

The 3 Skills That Separate Real AI Engineers from Prompt Engineers

1. Production RAG pipeline experience: Ask them to describe the last RAG system they built. They should mention: chunking strategy choices (fixed vs semantic), embedding model selection trade-offs, vector database indexing (HNSW vs IVF), re-ranking (cross-encoders), and retrieval SLA in milliseconds. If they can't explain why they chose a specific chunk size, they haven't shipped RAG in production.

2. Fine-tuning with LoRA/QLoRA: Ask them to describe the difference between full fine-tuning and LoRA. Ask what rank they'd choose for a domain-specific classification task and why. Ask about training data requirements (minimum token counts, data quality criteria). Most candidates who claim fine-tuning experience have only run hugging face notebooks not shipped fine-tuned models in production.

3. Eval frameworks: Ask how they measure hallucination rates in a customer-facing LLM application. They should mention: RAGAs or custom eval pipelines, LLM-as-judge patterns, human evaluation sampling rates, and how they define and track a hallucination rate KPI. This is the most important skill and the most commonly faked.

The 4-Stage Hiring Process That Finds the Top 5%

Stage 1: CV screen (30 min). Filter for: GitHub with real LLM projects (not tutorials), Hugging Face profile with model uploads, specific model names used (not just 'GPT-4'), and production deployment context (latency, scale, cost metrics).

Stage 2: Technical phone screen (45 min). Ask: 'Walk me through the last AI system you shipped to production users.' The answer reveals depth instantly. Follow up on every vague claim.

Stage 3: Take-home task (4 hours). Provide a real problem: build a RAG pipeline over a given corpus, with specific latency and accuracy requirements. Evaluate: code quality, eval methodology, and how they handle edge cases.

Stage 4: System design interview (60 min). Design a production LLM application end-to-end: data pipeline, model selection, serving infrastructure, monitoring, cost optimisation. Assess whether they think in systems, not just models.

What to Pay AI Engineers in India in 2026

Junior AI Engineer (1-2 years): ₹12-18 LPA. Can fine-tune models, build basic RAG pipelines, work within defined architecture.

Mid-level AI Engineer (3-5 years): ₹20-35 LPA. Owns AI architecture decisions, manages eval frameworks, optimises inference costs.

Senior / Staff AI Engineer (5+ years): ₹40-70 LPA. Defines AI strategy, leads multi-model architectures, mentors teams.

ML Ops / AI Infrastructure: ₹25-50 LPA. Specialises in serving, monitoring, and cost optimisation rarer than model builders and often the bottleneck.

Alternative: Shoppeal Tech provides dedicated offshore AI teams at 40-60% of equivalent in-house cost, fully managed.