Hire an LLM Engineer
Hire an LLM engineer to ship production language-model features — agents, tool-calling, RAG, evals, and cost/latency control — on backends that hold up. Remote, senior, ex-CTO.
If you want to hire an LLM engineer who has actually run language models in production — not just wired up a demo — that’s the work I’ve been doing lately. I’m Kuldeep Pisda, a senior backend engineer and former startup CTO in Bengaluru. I’ve shipped systems with multiple LLMs running in production, including real-time AI voice agents, and I care about the unglamorous parts: latency, cost, evals, and what happens when the model is wrong.
What I help with
- LLM agents and tool-calling — function/tool schemas, multi-step flows, and the state handling that keeps an agent from wandering off.
- RAG and semantic search — retrieval over your own data with pgvector, chunking and embedding strategy, and reranking. (There’s a dedicated RAG page if that’s the core need.)
- Evals and guardrails — measuring output quality instead of eyeballing it, plus validation, retries, and fallbacks for when a model returns garbage.
- Latency and cost control — streaming, caching, model routing (cheap model first, escalate when needed), and token budgets that keep the bill sane at scale.
- Real-time voice — I’ve built production voice agents, so I know the streaming/interrupt/latency constraints that text-only work never surfaces.
- The backend around the model — the boring 90%: queues, retries, observability, and a PostgreSQL layer that doesn’t fall over.
Why the backend matters
Most “AI” failures in production aren’t model failures — they’re plumbing: a timeout, an N+1 query feeding the prompt, no retry on a flaky provider, no way to measure regressions. I bring 6+ years of Django/PostgreSQL experience to LLM work, so the model sits on infrastructure that can actually carry it.
How engagements work
A focused build, an architecture review of an LLM feature you’re unsure about, or ongoing help hardening something from demo to production. Remote, IST, used to US/EU overlap.
FAQ
Which providers and models do you work with? Provider-agnostic — OpenAI, Anthropic, and open models. I’ll help you pick on cost, latency, and quality for your task rather than hype.
Can you take our prototype to production? That’s the most common ask. The gap between a working demo and something reliable is evals, error handling, and cost/latency work — exactly what I focus on.
Do you do RAG specifically? Yes — see hire a RAG engineer. I build retrieval on PostgreSQL/pgvector as well as dedicated vector stores.
Can you control our LLM costs? Usually — model routing, caching, prompt trimming, and batching typically cut spend without hurting quality.
Do you only do AI now? No — I’m a backend engineer who does AI. Most of my LLM work is anchored in solid Django/Postgres systems.
Let’s talk
Tell me what you’re trying to build with LLMs — get in touch, or grab a time below:
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