Looking to hire an AI engineer in India who ships production systems, not demos? I’m Kuldeep Pisda — a senior backend and AI engineer and former startup CTO based in Bengaluru. For 6+ years I’ve put AI in front of real users: multi-LLM orchestration behind a unified layer, retrieval over hundred-thousand-document knowledge bases, semantic search on pgvector, and agentic voice and SMS automation — all on backends built to stay up, stay cheap, and stay correct. The interesting part of AI work isn’t the prompt; it’s everything around it.

What I build

LLM agents — task-oriented agents with tool use, structured outputs, and guardrails, wired into real business workflows rather than a chat box.
RAG and semantic search — retrieval pipelines over large document sets, chunking and embedding strategies, and re-ranking that actually improves answers. I’ve tuned pgvector for 100k+ documents and built Indic/Hindi semantic search with multilingual SBERT.
Voice and SMS agents — agentic phone and messaging automation with Deepgram speech-to-text, turn-taking, and fallbacks for when a model stalls mid-call.
Multi-LLM orchestration — Claude, GPT-4o, Gemini, and Perplexity behind one unified layer, with retry/backoff, provider failover, and per-tenant model selection so each customer runs the model that fits their cost and quality bar.
Evaluation and reliability — prompt pipelines with versioning, eval sets to catch regressions, latency and cost budgets, and observability so you know why a response was wrong, not just that it was.

Experience you’re hiring

My most recent AI work is a full public case study: fifteen months embedded in an AI voice-agent platform — an AI receptionist for home-services contractors — where I was a top-15 contributor in a 120-developer monorepo and took a follow-up calling product from self-serve v1 to GA in about five weeks. That’s what production AI means in practice: idempotent durable jobs, CRM sync that honours rate-limit headers instead of guessing, and category-scoped SMS opt-out (A2P 10DLC compliance) shipped end to end. The thread goes back further — the company I co-founded built e-Paarvai, a real-time cataract-detection platform for the Tamil Nadu government that won a NASSCOM AI Gamechanger award, and I’ve built an in-house OTT media pipeline plus an AI content-intelligence layer (RAG and Hindi semantic search over 100k+ documents) for a platform serving 30,000+ users. The backends under all of this are the boring, load-bearing part — Django/DRF and FastAPI, PostgreSQL with pgvector, Celery, Inngest — and I speak about them at DjangoCon US, DjangoCon Europe, and EuroPython.

How engagements work

Short, well-scoped engagements with a written plan and testable milestones — a fixed-scope AI build, an ongoing retainer, or an architecture review before you commit to a direction. Remote-first and async-friendly, based in India (IST) and used to overlapping with US and EU hours.

FAQ

What does “production AI” actually mean? It means the feature survives real traffic: it handles a model timing out, a provider rate-limiting you, a malformed response, and a cost spike — without paging you at 2am. Demos ignore all of that. Production is the difference.

Which models do you work with? Claude, GPT-4o, Gemini, and Perplexity, plus Deepgram for speech-to-text. I put them behind a unified layer so switching or mixing providers is a config change, not a rewrite — and so each tenant can run the model that fits their needs.

RAG or fine-tuning? Usually RAG. For most business problems, grounding a strong general model in your own documents is cheaper, faster to update, and easier to debug than fine-tuning. I’ll tell you honestly when fine-tuning genuinely earns its cost — it’s rarer than the hype suggests.

How do you handle cost and latency? By treating them as budgets, not afterthoughts: caching, right-sizing the model per task, streaming responses, and measuring token spend per request so it doesn’t quietly balloon. Cheaper and faster usually come from architecture, not from a smaller model.

Can you add AI to an existing product? Yes — most of my AI work bolts onto live systems. I’ll map your data and traffic first and integrate carefully, rather than dropping in an unbounded LLM call and hoping.

What does it cost? Most engagements start with an audit of your AI stack — model integrations, pipelines, and the backend under them — at ₹2,00,000 + GST: written findings with ranked fixes in 7 calendar days, plus a 60-minute walkthrough call. Fixed-scope builds are quoted in writing after the audit, and the audit fee is credited toward any build started within 60 days. If you just need a quick call on a specific decision, a second opinion is ₹30,000 + GST — one 90-minute call and a written recommendation within 48 hours. Pricing shown is for India-registered companies; international clients are billed in USD — see services.

Let’s talk

Tell me what you’re trying to build and where it’s stuck — get in touch, or grab a time below:

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