SECAI Core · phase 9 of 15
Machine learning engineer
A role that takes prototypes and transforms them into production-grade models by optimizing the code, packaging dependencies, writing inference services, and designing evaluation tests that can withstand traffic
The Explain card
- Plain English
- A machine learning engineer turns prototypes into production-grade models: optimising the code, packaging dependencies, writing inference services and designing evaluation tests that can withstand real traffic.
- Example
- Handed a data scientist's notebook, the ML engineer containerises the model, pins and scans its dependencies, exposes it through an authenticated inference API, and builds load and regression tests so a new version cannot ship if accuracy or latency slips.
- Why it matters
- The jump from notebook to production is where vulnerable libraries, unvalidated inputs and unauthenticated endpoints creep in. A security-aware ML engineer closes those doors.
- Hook
- Takes the science experiment and makes it survive contact with users.
Where it sits in the deck
Phase 9: Roles, Teams, and Organisational Accountability
Governance frameworks are executed by people — introduce the human roles and organisational structures responsible for building, operating, and auditing AI systems.
