Advisor specialty
ML Platform Engineer
Builds the internal platform that lets your teams train and ship models themselves.
This describes a type of expertise, not a specific person. Ask us and we will tell you who in our network fits your initiative.
Problems solved
- Models work in a pilot but never reach production: reliable, monitored AI running in production.
- AI needs compute and platforms the current setup cannot give: infrastructure sized and secured for AI.
Typical deliverables
- Hands-on build, tested and handed over to your team
- Platforms
- Databricks, Microsoft Azure, Amazon Web Services, Google Cloud
- Examples only. Not every specialist uses every platform.
- Engagement type
- Implementation
- Selection criteria
- Suggested: 5 to 8 years of relevant hands-on experience.
- A guideline, not proof of seniority.