The short read
Generative AI engineers connect language models to applications and information. Retrieval-augmented generation (RAG) finds relevant material to ground responses; MLOps covers the practices that help teams deploy, observe and maintain models and AI features.
Role explained
What the work involves
The work may sit within software, data, product or platform teams. A strong candidate understands when a model is useful, how to evaluate its output and how to handle privacy, latency, cost and failure cases.
Typical outputs
- A model-powered feature tied to a clear user task
- Retrieval and evaluation that can be explained
- Monitoring, fallback and data safeguards
Skills and examples to demonstrate
- Python, APIs and practical software engineering foundations
- Evidence of retrieval quality, evaluation and operational awareness
- Clear limits, safe data handling and human review where appropriate
Your Talent Passport
Make your work easy to understand
- Describe the user problem and your specific contribution.
- Distinguish a prototype from a pilot or production system.
- Show how you tested quality and handled incorrect or incomplete answers.
Make a plan
A practical 2027 learning checklist
Build a small RAG feature over public, well-scoped material.
Create test questions that expose retrieval and answer failures.
Add logging, a fallback path and clear data boundaries.
Practise explaining model trade-offs in ordinary language.
Check the location and work mode
Where these roles are found
AI work can appear in product, services and GCC teams in Bengaluru, Hyderabad, Pune, Chennai and Delhi NCR. Location does not establish a role’s seniority or hiring volume; check current descriptions for the actual team and responsibilities.