The short read
Generative AI engineers connect large language models to applications and useful information. Retrieval-augmented generation (RAG) brings relevant, trusted material into a model’s response. Agentic workflows let a model use tools or take bounded steps, with permissions and human review designed in.
Role explained
What the work involves
MLOps and evaluation work makes models and AI features testable, deployable and observable. Python, APIs, data handling, quality checks and safe fallback behaviour help turn experiments into dependable software.
Typical outputs
- Grounded answers with retrieval and evaluation
- Tool-using workflows with explicit permission boundaries
- Model services with monitoring, fallback and human review
Skills and examples to demonstrate
- Can explain when RAG, fine-tuning or a simpler search is appropriate
- Tests output quality, failure cases, data access and latency
- Describes safe limits and where a person remains responsible
Your Talent Passport
Make your work easy to understand
- Describe the task, your contribution and how you measured whether the AI feature helped.
- Name the model, retrieval or deployment tools only in the context of work you actually did.
- State whether a project was a prototype, pilot or production system; do not share confidential prompts or data.
Make a plan
A practical 2027 learning checklist
Build a small RAG application over public documents and test retrieval quality.
Create an evaluation set that includes incorrect, incomplete and unsafe-answer cases.
Practise a bounded tool workflow with clear permissions, stop conditions and human hand-off.
Strengthen Python, APIs, data handling, monitoring and basic model operations.
Check the location and work mode
Where these roles are found
Bengaluru and Hyderabad have large technology and GCC ecosystems; AI work also sits within product, services and data teams in other established hubs. Check each role’s team and responsibilities rather than relying on a city label.