AI infrastructure engineers
The people who make inference affordable. GPU capacity, serving architecture, batching and routing decide whether your AI product has a business model or an expensive hobby.
- Typical seniority
- Senior through principal
- Core stack
- Kubernetes, vLLM, Triton, TensorRT, Ray
- Focus
- Throughput, latency, GPU utilisation, unit cost
- Engagement shapes
- Fixed-scope assessment, then embedded
- Typical duration
- Two to nine months
- Deliverable
- A serving stack with a defensible unit cost
AI infrastructure engineers from CloudOps Network design and operate the systems AI workloads run on: GPU capacity planning, inference serving, model gateways, batching and throughput optimisation, and cost per token or per request. They are matched on production inference experience at scale.
What AI infrastructure engineers do on our engagements
GPU capacity planning
Instance selection, reservation and spot strategy, and honest forecasting of what your traffic actually requires.
Inference serving
Serving stack selection and tuning, continuous batching, quantisation trade-offs and tail latency control.
Model gateways
A routing layer across providers and self-hosted models with fallback, rate limiting, key management and usage attribution.
Throughput optimisation
Tokens per second per dollar, measured properly, improved deliberately, and reported in terms finance recognises.
Multi-tenancy
Isolation, fair queuing and per-tenant limits, so one customer cannot consume the cluster.
Observability for inference
Queue depth, batch efficiency, GPU utilisation and cost per request, visible in real time.
Self-host or use an API?
The honest answer depends on volume, latency requirements, data boundaries and how much operational appetite you have. Below a certain request volume, self-hosting costs more once engineering time is counted properly.
A good AI infrastructure engineer will model both and show you the crossover point rather than assuming one.
Volume
Sustained high throughput is where self-hosting starts to win on unit cost.
Latency
Dedicated capacity gives predictable tail latency that shared endpoints cannot promise.
Data boundaries
Sometimes residency or contractual constraints make the decision for you.
Operational cost
A GPU cluster is a system someone has to run. Count that engineer in the model.
A sample of the bench
Availability changes weekly. This is the shape of who is in the network, not a live list.
Illustrative profiles drawn from the current roster. Names and clients are withheld until a mutual NDA is in place; the shortlist you receive names real people with real references.
Questions about hiring AI infrastructure engineers
Do we need this role, or will a DevOps engineer cover it?
A strong DevOps engineer covers the Kubernetes and autoscaling layer. Serving stack tuning, batching strategy, quantisation trade-offs and GPU utilisation are a distinct specialisation, and the cost difference between good and average here is large.
Can you reduce our current inference spend?
Usually. Batching configuration, model routing, quantisation and right-sizing commonly produce substantial reductions, and the work starts with measurement rather than with a recommendation.
Do you work with reserved GPU capacity?
Yes, including reservation and spot strategy, and the capacity forecasting that stops you committing to the wrong shape.
Which serving stacks do your engineers use?
Commonly vLLM, Triton and TensorRT-LLM, plus Ray for distributed workloads. Selection follows the workload rather than preference.
Tell us what you are building
Describe the work in a few lines. We come back with a shortlist of engineers who have done it before, their availability, and a rate range. No account, no platform fee, no obligation to hire.
Most briefs get a shortlist within three business days.