We are building an AI documentation platform that uses Azure Speech-to-Text and Azure OpenAI for clinical dictation and transcription.
No Azure commitment tiers enabled yet
What we want now is a specialist review and guidance focused purely on cost optimisation, not general setup.
This is a consulting/advisory role, not a long-term dev engagement.
What we need help with:
- We are looking for an expert who can help us:
Review our current use of Azure Speech-to-Text (dictation vs transcription workloads)
- Review Azure OpenAI usage patterns (prompt structure, token usage, model selection)
- Identify practical ways to reduce per-hour or per-request costs without degrading clinical quality
Advise on:
- batching strategies
- streaming vs async transcription
- prompt/token efficiency
- diarisation trade-offs
- workload separation (dictation vs full transcription)
- evaluate when Azure commitment tiers make sense and how to size them safely
- sanity-check whether alternative Azure configurations or patterns could materially reduce spend
We are not looking to switch cloud providers at this stage.
What this is NOT:
❌ Not a beginner Azure role
❌ Not an OpenAI “how to call the API” task
❌ Not general cloud architecture advice
❌ Not prompt engineering for output quality
We are specifically optimising cost at scale.
You’re a great fit if you have:
- Deep hands-on experience with Azure OpenAI Service
- Real-world experience with Azure Speech-to-Text at scale
- Experience optimising AI workloads for cost, not just performance
- Experience advising SaaS or healthcare platforms
- Strong opinions backed by numbers
Bonus (not required):
- Experience with medical / clinical transcription workloads
- Experience with Azure commitment pricing in production
Please include:
- Examples of similar cost optimisation work you’ve done
- Azure services you’ve worked with in production
- Any relevant metrics you improved (e.g. cost reductions, efficiency gains)
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