Hire AI Engineer — RAG Pipelines, Agents & Evals
AI engineering is not demos — it is RAG pipelines that retrieve chunks, agents that do not spiral, and evals that catch regressions early. I build LLM systems: document Q&A over your knowledge base, agents with tool use, and model selection driven by cost and latency. Every system ships with harnesses, guardrails, and monitoring.
I'm Omer Muneer Qazi, a Dubai-based Fractional CTO & Solutions Architect with 15+ years of experience and 100+ projects delivered across 6 countries. I scope AI work through a structured hire process and you can reach me directly via the contact page.
What an AI Engineer Actually Delivers
RAG Pipelines That Retrieve
Chunking strategies, hybrid search, and reranking tuned against your actual documents — not defaults. I build ingestion pipelines with eval sets so retrieval quality is measured, not assumed.
Production LLM Agents
Agents with defined tools, bounded loops, and human-in-the-loop checkpoints for risky actions. I design for reliability: automatic retries, timeouts, and fallback paths when the model misbehaves.
Evaluation Harnesses
Golden datasets, automated scoring, and regression suites that run on every prompt or model change. You will know exactly what improved and what broke before anything reaches production.
Vector DB & Data Architecture
Embedding model selection, index design, and metadata filtering architected for your scale. I keep retrieval latency low and costs predictable as your corpus grows into millions of chunks.
Model Selection & Cost Engineering
Model choice driven by benchmarked quality against your tasks and real cost-per-request math. I route between models by difficulty so you stop overpaying for simple queries.
Guardrails & Monitoring
Prompt injection defenses, output validation, PII handling, and production monitoring with alerting. Your AI system gets the operational maturity the rest of your stack already has.
How the Engagement Works
A structured engagement with no surprises — you’ll always know what’s happening and what’s next.
Use-Case Scoping
We define the exact job the AI must do, the data it can touch, and how success is measured. Vague “AI-powered” ideas become concrete specs with acceptance criteria.
Prototype & Eval Baseline
I build a working prototype against your data and establish eval baselines. You see real outputs on real inputs within the first two weeks.
Harden for Production
Retrieval tuning, guardrails, error handling, and monitoring get built in. The system graduates from impressive demo to dependable infrastructure your team can rely on.
Handover & Playbooks
You get documented architecture, eval suites your team can run, and operational playbooks for model updates, so the system improves after I leave.
Why Work With Omer on AI Engineering
AI projects die between the demo and production — great in demos, broken at scale. With 15+ years across 100+ projects in 6 countries, I engineer for the long tail: evals, guardrails, and cost controls from day one. You get AI systems that survive contact with users and real data.
I work as a fractional CTO: direct communication, working prototypes early, and honest guidance on where AI genuinely helps versus where it does not. If you have a use case in mind, get in touch to scope it.
Frequently asked questions
How long does a RAG system take to build?
A working prototype over your documents takes two to three weeks. Production hardening — evals, guardrails, monitoring — adds another three to four weeks depending on data complexity.
Which LLM models do you recommend?
It depends on your task, latency needs, and budget. I benchmark candidate models against your eval set and recommend based on measured quality per dollar, not hype.
Can you work with our existing data?
Yes. I build ingestion pipelines for PDFs, wikis, databases, and APIs, handling messy real-world data: scanned documents, inconsistent formatting, duplicate records, legacy exports, and multilingual content.
How do you prevent hallucinations?
Layered defenses: grounded retrieval with citations, output validation against sources, confidence thresholds, and human review for high-stakes actions. Evals measure the residual risk continuously in production.
Do you fine-tune models?
When it pays off. Most business use cases win more from better retrieval and prompting than fine-tuning. I recommend fine-tuning only when evals show a clear, measurable gap it would close.
Ready to Hire Your AI Engineer?
Describe your use case and data sources. You will get a feasibility assessment, architecture plan, and fixed quote within days.