Hire vetted senior OpenAI engineers
24h
first response
72h
from brief to shortlist
1 in 7
clears our vetting
75/100
the pass mark
Hire senior OpenAI engineers through Mahala. Vetted specialists in production GenAI on the OpenAI API: retrieval, function calling, evaluation, guardrails, and token-cost control, matched to your stack in 72 hours. Every profile scored against our vetting protocol before it reaches your shortlist.
What a wrong OpenAI hire costs
A wrong OpenAI hire ships a demo that dazzles and a production system that hallucinates, leaks the system prompt, and runs an unbounded token bill. The gap between a convincing prototype and a production GenAI system is evaluation, guardrails, retrieval grounding, and cost control. That gap is exactly where most GenAI projects die, and exactly what a demo never shows you.
Anyone can call the OpenAI API in a weekend. The question is whether they have run one in production: an evaluation harness that catches regressions, guardrails for when the model misbehaves, retrieval that grounds answers in your data, structured outputs and function calling that other systems can trust, and token-cost management that keeps the bill predictable. That is what we vet OpenAI engineers on.
What our OpenAI engineers deliver
01
Production GenAI on the OpenAI API: retrieval-augmented generation grounded in your data, function calling, and structured outputs.
02
Evaluation harnesses that catch regressions before they reach users, and guardrails for when the model misbehaves.
03
Token-cost control: model selection per task, caching, prompt compression, and fallback handling for rate limits.
04
Observability for GenAI: tracing every call, logging failures, and monitoring quality over time.
How to recognise a
strong OpenAI engineer
- They build an evaluation harness before they ship, and can show how it catches regressions.
- They select the model per task instead of using the top tier for everything, and can defend the token budget.
- They ground answers in your data with retrieval, and treat guardrails as part of the system, not a patch.
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 AI Engineer, six years, most recently primary counterpart to the CEO on a regulated healthcare deployment. Built the production GenAI assistant on the OpenAI API for a top-three European insurer: retrieval grounding, a full evaluation harness, guardrails, and token-cost controls that cut spend 40% without losing quality. Deployed across 200+ facilities at 99.2% uptime. Strong on OpenAI API, Python, LangChain, vector databases, and observability tooling. Available remotely across Europe and the GCC, contracted via Mahala.ai. Vetted at 92/100.
Related
questions
How do you stop an OpenAI system from hallucinating in production?
Retrieval grounding, an evaluation harness that scores answers against known-good references, and guardrails that catch and contain misbehaviour. Our OpenAI engineers treat evaluation as a first-class part of the system, not an afterthought. We assess this directly in vetting.
How do you keep OpenAI token costs predictable?
Model selection per task (not GPT-4 class for everything), response caching, prompt compression, and fallback handling for rate limits. Cost control is part of production GenAI, and part of the vetting bar.
Can they build RAG systems?
Yes. Retrieval-augmented generation grounded in your data is the most common OpenAI engagement. Our engineers pair the OpenAI API with a vector database (such as Qdrant) and an evaluation harness for retrieval quality.
Do they handle data residency and privacy concerns?
Yes. For teams that cannot send data to an external API, our engineers also work with self-hosted models. See our vLLM specialists. We confirm the data-residency requirement in the brief.
Do they work in regulated environments such as VDI or Citrix?
Yes. Most of our enterprise placements operate in regulated remote environments. We confirm access requirements in the brief.
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.