Question 1

How much does it cost to hire AI engineers in India in 2026?

Accepted Answer

Part-time AI engagements from Birtik Tech start at ₹1L/month. Full-time dedicated AI engineers start at ₹2L/month (roughly $2,400). Squads of 3–6 engineers with an AI tech lead start at ₹8L/month. AI engineering rates are higher than general full-stack because the supply of senior ML talent in India is genuinely tighter - but still 50–70% below US rates.

Question 2

Do your AI engineers build production systems or just prototypes?

Accepted Answer

Production systems. Every AI engagement ships with an evaluation harness - labelled golden datasets, automated regression scores, prompt versioning, and human-in-the-loop review for high-stakes outputs - plus cost monitoring, inference scaling, and observability. Prototypes that look impressive in a demo and fail silently in production are the failure mode we design against.

Question 3

Do your AI engineers build RAG (retrieval-augmented generation) pipelines?

Accepted Answer

Yes - RAG is our most common AI engagement. Vector database selection (Pinecone, Weaviate, pgvector, Qdrant), chunking strategy, embedding model selection, hybrid retrieval, re-ranking, and end-to-end evaluation are all part of standard delivery, not add-ons. We design RAG so the answer-quality is measurable, not just plausible-looking.

Question 4

Can your engineers fine-tune open-source models (Llama, Mistral, Qwen)?

Accepted Answer

Yes. LoRA, QLoRA, and full fine-tunes on Llama, Mistral, Qwen, and other open-source families for use cases where commercial APIs are too expensive, too slow, or too generic. We also handle the harder side - eval-set design, regression testing, and the call on whether fine-tuning is genuinely worth it versus better prompting and RAG.

Question 5

Do your AI engineers use OpenAI, Anthropic Claude, Gemini, or open-source models?

Accepted Answer

All four. We pick the model that matches your latency budget, cost ceiling, quality bar, and data-residency needs. Frontier models (GPT, Claude, Gemini) for highest quality; open-source models on AWS Bedrock or Vertex AI for cost-sensitive or sensitive-data workloads. We architect to swap models cleanly so you are not locked in.

Question 6

Do your engineers handle computer vision and document intelligence?

Accepted Answer

Yes. We place CV engineers for defect detection, OCR, video analytics, invoice and contract extraction, and embedded vision pipelines for industrial and retail use cases. Stack typically includes YOLO, OpenCV, Hugging Face vision transformers, and structured-output guarantees on top of OCR plus LLM pipelines.

Question 7

How do your engineers handle AI privacy, compliance, and data residency?

Accepted Answer

We design AI workflows with DPDP, GDPR, and HIPAA-aligned controls - consent capture, data residency in Indian or EU cloud regions when required, PII redaction, prompt-injection defences, and audit logging. Your data stays in environments you control and is never sent into third-party training pipelines.

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