Hire LLM Engineer — AI features that survive real users
A chatbot demo takes an afternoon; a production LLM feature takes engineering. Real systems need structured outputs that never break your parsers, function calling wired to your APIs, context assembled within token budgets, guardrails against prompt injection, and evaluation harnesses that catch regressions before users do. An LLM engineer builds that layer — the difference between a party trick and a feature you can ship.
I'm Omer Muneer Qazi, a Dubai-based Fractional CTO & Solutions Architect with 15+ years of experience and 100+ projects delivered across 6 countries. My engineers ship LLM features with evals and cost controls from day one. If your product needs grounded answers from your own data, hire a RAG developer through me for the retrieval layer.
Production LLM features, engineered end to end
Prompt & context engineering
System prompts, few-shot examples, and context assembly designed as versioned artifacts — tested and refined against real inputs, not vibes, with regression sets that run on every change.
Structured outputs & function calling
JSON-mode outputs, schema validation, and tool calling wired to your APIs so the model acts inside your systems reliably — every response parseable, every action auditable.
Retrieval integration
Your documents, tickets, or product data connected as grounded context, with chunking and ranking tuned to your corpus — so answers cite your sources instead of inventing them.
Evaluation harnesses
Golden datasets and automated evals that score accuracy, faithfulness, and tone on every iteration — the test suite that keeps model upgrades and prompt tweaks from silently breaking quality.
Guardrails & safety
Prompt-injection resistance, PII redaction, content filters, and human-in-the-loop gates placed where the risk actually is — proportional protection, not theater.
Cost & latency optimization
Model tiering, semantic caching, prompt compression, and streaming responses that keep per-request costs predictable and p95 latency inside your UX budget.
From prototype to production feature
A structured engagement with no surprises — you’ll always know what’s happening and what’s next.
Feature scoping
We define the exact behavior, inputs, outputs, and the eval set that proves it works — including the edge cases where the model will struggle.
Prototype & eval loop
Working prompts and tools iterated against your golden dataset, with quality scores tracked per iteration until they clear your bar.
Systems integration
The feature wired into your app: APIs, databases, auth, logging, and the guardrails layer — built like production software because it is.
Launch & monitoring
Gradual rollout with live quality sampling, cost dashboards, and a feedback loop that turns real user interactions into eval improvements.
Why hire a LLM engineer through a Fractional CTO
LLM features fail in production for predictable reasons: no evals, prompt-as-code chaos, context that grows until costs explode, no plan for model upgrades. I scope every engagement with evaluation and cost discipline built in — and I review the architecture myself before it ships.
If you have an AI feature that works in the demo and breaks in the wild, describe it to me and I will tell you what production-hardening it actually needs.
Frequently asked questions
What is the difference between an LLM engineer and a prompt engineer?
Prompt craft is one tool in the LLM engineer’s kit. The engineering role covers the whole system: retrieval, tool use, structured outputs, evals, guardrails, cost control, and integration with your stack — everything around the prompt.
Which model should our feature use?
It depends on the task’s reasoning demands, latency budget, and cost target. We usually prototype on a frontier model, measure, then downshift to the cheapest model that holds quality — sometimes a smaller model with good retrieval beats a big one.
How do you prevent hallucinations?
Grounding plus verification: retrieval from your sources, structured output schemas, and eval sets that specifically test faithfulness. For high-stakes outputs, a human-review gate or a second verification pass is part of the design.
What does an LLM feature cost to run?
Roughly: tokens per request times requests times model price, minus caching and tiering savings. A typical support-assistant request costs fractions of a cent to a few cents; I model your exact numbers before build.
Can you migrate us from one model provider to another?
Yes — that is a core part of the job. We abstract the provider behind a clean interface, port the evals, and re-run them against the new model so quality is verified, not assumed.
Ship your AI feature properly
Tell me what the model needs to do and where it plugs into your product — I will scope the build with evals and cost controls included.