Hire AI Model Deployment Expert — inference infrastructure that scales without surprises
Running AI models in production is an infrastructure discipline, not a demo script: GPU provisioning that does not bankrupt you, inference servers tuned for your latency budget, quantization that trades precision for speed without breaking quality, autoscaling that handles spikes, and monitoring that catches degradation before users do. An AI model deployment expert builds the serving layer so your models stay fast, available, and affordable at real traffic.
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 have shipped systems where milliseconds and cents per request decide the business model. When you need the model behavior built too, hire an LLM engineer through me for the application layer.
Inference infrastructure, engineered for economics
Serving architecture
vLLM, TensorRT-LLM, or managed endpoints evaluated against your latency, throughput, and cost targets — with a serving design that fits your traffic shape, not a generic template.
GPU strategy & sizing
Right-sized GPU selection with spot, reserved, and on-demand blending, so you are not paying for A100s to serve a workload a smaller card handles at half the cost.
Quantization & optimization
AWQ, GPTQ, or FP8 quantization benchmarked against your quality evals — speed and cost gains verified not to break what the model is for.
Autoscaling & load handling
Scale-to-zero for bursty workloads, warm pools for steady ones, request batching and queueing that keep p99 latency inside your budget during spikes.
Observability
Token throughput, latency percentiles, error rates, and GPU utilization on live dashboards, with alerts that fire before users notice degradation.
Cost governance
Per-request and per-tenant cost tracking with budgets and circuit breakers — so a traffic spike or a runaway client cannot turn into a five-figure surprise.
From model to production endpoint
A structured engagement with no surprises — you’ll always know what’s happening and what’s next.
Workload profiling
We characterize your traffic: request shapes, latency budget, concurrency, and the quality bar the serving setup must not break.
Serving design
Infrastructure sized and benchmarked against your workload — including a load test that proves the latency numbers before launch.
Deployment
The serving stack deployed with CI/CD, model versioning, and rollback procedures, because models get updated and updates must be reversible.
Operate & optimize
Monitoring live, costs tracked per request, and continuous tuning as your traffic pattern evolves.
Why hire a AI model deployment expert through a Fractional CTO
Inference bills are where AI projects bleed: oversized GPUs, no caching, no autoscaling, no cost attribution. I treat serving as an economic system — every millisecond of latency and every cent per request accounted for — and I verify quality survives every optimization.
If your AI feature works but costs too much or responds too slowly, tell me your traffic and latency budget and I will scope the serving fix.
Frequently asked questions
Should we self-host or use managed model APIs?
Managed APIs win on speed to market and zero ops burden; self-hosting wins when per-request costs at your volume exceed GPU costs, or when data cannot leave your network. We run the numbers on your traffic before recommending.
What latency can we realistically expect?
For LLM inference: time-to-first-token under a second and tens of tokens per second are achievable with proper serving on good hardware. Your exact budget depends on model size and GPU choice — we benchmark it.
How do we handle traffic spikes?
With a mix: autoscaling for sustained growth, request queueing with graceful degradation for bursts, and semantic caching that absorbs repeated queries without touching the GPU.
Does quantization hurt quality?
Done properly, barely — 4-bit quantization typically costs single-digit percentage points on benchmarks, which often does not matter for your task. We verify against your evals, not public benchmarks, before accepting any trade.
Can you deploy on our cloud or on-prem?
Yes — AWS, GCP, Azure, or your own hardware. The serving stack is portable; what changes is procurement, networking, and how we handle GPU availability.
Fix your inference economics
Share your model, traffic, and latency budget — I will design a serving setup that hits the numbers without the waste.