Hire LLM Application Developer
engineered for production
There's a canyon between a prompt that works once and an LLM application that works for ten thousand users. When you hire an LLM application developer, you need someone who engineers across that canyon: retrieval architecture, evaluation harnesses, prompt versioning, streaming UX, cost control, and the fallbacks for when models misbehave. That production engineering is what my AI/ML integration work at EverestX was about.
View AI Engineer CV ↗ PDF Word
I build LLM applications as real software: versioned, tested, monitored, and maintainable. RAG over your company knowledge, copilots inside your product, internal tools that reason over your data. See the broader AI and ML practice and engagement models on the hire page.
What You Get
Every engagement is scoped around concrete deliverables — here's what a typical llm application development engagement includes.
RAG architecture
Document ingestion, chunking strategy, embeddings, and vector retrieval tuned to your content — the foundation every serious LLM app needs.
Evaluation harness
Test sets and scoring for your app's actual tasks, so regressions are caught before users see them — not after.
Copilot UX
Streaming responses, citations, suggested follow-ups, and graceful degradation designed in React with real product polish.
Tool use and actions
Function calling that lets the app act — querying your APIs, updating records — with permissions and audit trails.
Prompt and model management
Versioned prompts, model routing across providers, and A/B testing of model behavior like any other product surface.
Production operations
Latency budgets, token cost controls, caching, fallbacks, and observability dashboards from day one.
How It Works
A structured engagement with no surprises — you'll always know what's happening and what's next.
Technical discovery
We define the app's job, its users, data sources, and the quality bar. I assess whether RAG, fine-tuning, or agents fit — honestly.
Architecture and prototype
Retrieval design plus a working prototype against your real data within the first weeks, with an eval set defined up front.
Build with evals
Full application development with the evaluation harness running continuously — quality is measured, not hoped for.
Launch and iterate
Staged rollout, usage analytics, and iteration cycles against the evals and real user feedback.
Why Hire Omer Muneer Qazi
I'm Omer Muneer Qazi, a Fractional CTO and Solutions Architect with 15+ years of experience, 100+ projects delivered across 6 countries, enabling $25M+ in revenue. My EverestX AI/ML integration work covered LLM systems in production — retrieval, evals, guardrails, cost engineering. I build across TypeScript, React, Node.js, and Python, and I've held senior roles at Phaedra Solutions, Integriti, Napollo, Nabidios, and Nello. Dubai-based, working globally.
Software engineering rigor
Your LLM app gets the same discipline as any product: tests, versioning, CI, monitoring. Prompts are code and treated that way.
Evaluation-driven quality
I define how 'good' is measured before building, then optimize against it — the difference between a demo and a product.
Pragmatic model choices
OpenAI, Anthropic, or open-source — selected for your latency, cost, and compliance needs, with routing so you're never locked to one provider.
Frequently Asked Questions
Straight answers to the questions I'm asked most about llm application development engagements.
RAG vs fine-tuning vs agents — which do we need?
Start with RAG: it grounds the model in your data without training costs and updates anytime. Fine-tuning fits consistent style or specialized tasks. Agents fit multi-step workflows. Most production apps I build are RAG plus tool use.
How do you measure LLM app quality?
With an eval harness: a curated set of real tasks scored on accuracy, groundedness, and task completion, run on every change. Plus production sampling with human review. If quality isn't measured, it silently degrades.
What about data privacy with LLMs?
Retrieval keeps your data in your infrastructure — only relevant snippets reach the model API. For strict requirements, Azure OpenAI or self-hosted open-source models keep everything inside your boundary.
How do you control costs at scale?
Caching repeated queries, routing simple tasks to cheaper models, compressing retrieval context, and per-user plus global budgets with alerts. Cost is an architectural concern, not a surprise.
Can you integrate the LLM app into our existing product?
Yes — the usual case. I build the LLM service as a clean API your product calls, with auth, rate limiting, and feature flags, so your team owns the integration surface.
Ready to get started?
Tell me about your llm application development needs — I'll reply within one business day with honest first thoughts and clear next steps.
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