Hire an LLM Engineer

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#

The multi-vendor problem isn’t hypothetical for me — I’ve written up running one Django codebase across four LLM vendors for an investor-matching platform: Claude, GPT, Gemini, and Perplexity behind a single tiny interface, with every tenant bringing their own API keys. The post covers the isolation traps that only show up in production — cache an LLM client keyed on vendor instead of tenant and you’ve built a cross-tenant data leak; fall back to a platform key and you’ve silently routed a customer’s private data through your own account. On the open-source side I build chi, an autoresearch harness that points a fleet of LLM coding agents — any vendor — at a problem with a programmatic evaluator and lets them iterate unattended. And most “AI” failures in production are still plumbing — timeouts, missing retries, no way to measure regressions — so I bring 6+ years of Django/PostgreSQL to make sure 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.

What does an engagement cost? Most LLM work starts with a $4,000 audit — written findings with ranked fixes in 7 calendar days plus a 60-minute walkthrough call — and the build itself is quoted in writing after it, with the audit fee credited if we start within 60 days. If you just need a second opinion on one decision, that’s $500 for a 90-minute call and a written recommendation within 48 hours. All prices in USD plus GST where applicable, and you’ll have the cost in writing before we start.

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:

Related: Hire an AI Engineer in India · Hire a RAG Engineer · Hire a Python Consultant · Hire a PostgreSQL Expert