AI rolesStart here
AI engineering roles at GCCs: the seven tracks explained
The seven AI engineering roles GCCs in India hire for, what each one builds, the skills each needs and how to choose the track that fits your background.
6 min read
"AI engineer" covers seven different jobs. These guides take each one in turn: what the person builds, the skills the role is hired on, who the role suits and how its interviews differ, so you prepare for one target and not for all of them.
8 guides
Explore the rolesThe seven AI engineering roles GCCs in India hire for, what each one builds, the skills each needs and how to choose the track that fits your background.
The AI architect role at a GCC explained: scope, system design, build versus buy, stakeholder work, the skills expected and how senior engineers move into it.
What an AI backend engineer does at a GCC, why Java, .NET and Node developers fit the role, the skills to add and how to make the switch with proof of work.
What an AI data engineer does at a GCC: pipelines, data quality and features for models, the skills needed, and how ETL and data engineers can move into it.
The applied scientist role at a GCC: how it differs from data scientist and ML engineer, the statistics and modelling depth expected, and who it suits.
What a GenAI engineer does at a GCC, the skills the role needs across RAG, agents, evaluation and serving, and a practical route into it for engineers.
The MLOps engineer role at a GCC explained: platform work versus model work, the skills it needs, and why it is a strong route for DevOps and cloud engineers.
The ML engineer role at a GCC explained: what you build week to week, the skills the role needs, who it suits and how ML engineers are usually interviewed.