Hire vetted senior vLLM engineers
24h
first response
72h
from brief to shortlist
1 in 7
clears our vetting
75/100
the pass mark
Hire senior vLLM engineers through Mahala. Vetted specialists in self-hosted LLM serving: throughput optimization, GPU utilization, and data-residency-compliant inference, matched to your stack in 72 hours. Every profile scored against our vetting protocol before it reaches your shortlist.
What a wrong vLLM hire costs
A wrong vLLM hire stands up a GPU cluster that sits at 20% utilization, serves requests one at a time when continuous batching would multiply throughput, and costs more per token than the OpenAI API it was meant to replace. Self-hosting an LLM only makes sense when it is done well. Done badly, it combines the worst of both worlds: high fixed GPU cost and low throughput.
Self-hosting an LLM is the answer for teams that cannot send data to an external API: banks, government, healthcare, anyone with data-residency rules. But the value only appears with real engineering. The question we vet for is whether they can run vLLM at high throughput: continuous batching, PagedAttention, tensor parallelism across GPUs, quantization, and an OpenAI-compatible endpoint so your application code does not change. This is where MLOps meets the GCC data-residency reality.
What our vLLM engineers deliver
01
Self-hosted LLM serving with vLLM: continuous batching and PagedAttention for high throughput.
02
Tensor parallelism and GPU utilization tuning so the hardware you pay for is the hardware you use.
03
Quantization (AWQ, GPTQ) to fit larger models on fewer GPUs without losing quality.
04
OpenAI-compatible serving so your application code does not change, plus autoscaling and observability.
How to recognise a
strong vLLM engineer
- They talk about GPU utilisation and continuous batching first, because that is where the economics live.
- They use quantisation to fit larger models on fewer GPUs without losing quality you can measure.
- They expose an OpenAI-compatible endpoint so your application code changes by a URL, not a rewrite.
How an
engagement runs
- Brief us: role, stack, project phase, timeline, access model (VDI).
- We match from our two-layer vetted bench.
- Two or three blind CVs within 72 hours of a clear brief.
- Interview the finalists, choose the best fit.
- Mahala handles contracting, screening where required, and onboarding. One contract, one monthly invoice.
NDA on request. If your brief involves sensitive detail about the project, the team, or the IP, we offer a preliminary NDA as a service. Not required to receive a shortlist.
Representative
profile
Senior MLOps Engineer, seven years in platform and infrastructure. Built the self-hosted LLM serving platform for a tier-one bank that could not send data to an external API: vLLM with continuous batching and tensor parallelism, AWQ quantization to fit a 70B model on four GPUs, and an OpenAI-compatible endpoint so the application team changed nothing. Held 3x the throughput of the naive setup at the same GPU cost. Strong on vLLM, Kubernetes, CUDA, Prometheus, and Terraform. Available remotely across Europe and the GCC, contracted via Mahala.ai. Vetted at 91/100.
Related
questions
When should we self-host an LLM instead of using an API?
When data residency or privacy rules mean you cannot send data to an external API. Common in GCC banking, government, and healthcare. Self-hosting with vLLM keeps inference inside your perimeter. Our engineers make that economical, which is the hard part.
How do you get high throughput from vLLM?
Continuous batching and PagedAttention are the core of vLLM. Add tensor parallelism across GPUs, quantization to fit larger models, and careful request scheduling. A well-tuned vLLM deployment can hold several times the throughput of a naive one at the same GPU cost.
Can they keep our application code unchanged?
Yes. vLLM exposes an OpenAI-compatible endpoint. Our engineers stand it up so your application that already calls the OpenAI API points at your self-hosted model with a URL change and nothing more.
Does this fit our VDI and data-residency requirements?
Yes. This is exactly the case self-hosting is built for. Inference runs inside your infrastructure, your data never leaves your perimeter, and our engineers work inside your VDI. We confirm the requirement in the brief.
Do they handle GPU cost optimization?
Yes. Quantization to fit models on fewer GPUs, utilization tuning so you are not paying for idle hardware, and autoscaling. GPU economics is part of the vetting bar.
How is it priced?
Senior delivery is priced on a day or hourly rate, depending on the engagement model. We share rate ranges during the first call.