RAG / Backend Engineer
Vector DBs, retrieval systems, production APIs
Indicative CTC
14.5 LPA
Indian market, not an offer
Interview rounds
5
typical loop
Track length
12 months
program structure
What the work involves
Day-to-day work, not the job description version.
- Build and maintain the retrieval pipeline behind a knowledge feature
- Keep retrieval fast while the document corpus keeps growing
- Handle access control, so users never retrieve documents they should not see
- Instrument the pipeline and attribute failures to the right stage
What employers test
The skills that decide whether you clear the loop.
- Strong backend engineering
- Indexing and search fundamentals
- Embeddings and vector databases
- Query understanding and ranking
- Instrumentation and latency budgets
Salary bands
Indicative Indian market ranges for this role, not Avolute-AI graduate offers. Bands, not guarantees.
11-15 LPA
Common for backend roles with a search component
15-20 LPA
The band most candidates in India land in
20+ LPA
Platform and infrastructure-heavy search teams
How the program covers it
Where this role sits across the 12 months.
Months 1-3
Python, data structures, and your first deployed retrieval system
Months 4-6
Chunking, embeddings, and hybrid search with reranking
Months 7-9
Retrieval evaluation, permission-aware access, observability
Months 10-12
A production RAG service with measured retrieval quality
The interview loop
What each round is actually checking.
- 01
Screening
Backend experience
- 02
DSA
Data structures, weighted heavily
- 03
Backend design
APIs, databases, caching
- 04
Search deep-dive
Chunking, ranking, relevance
- 05
Project deep-dive
Retrieval metrics and trade-offs
We run mock interviews on this structure from month seven, including a full-length system design round timed the same way a real one is.
Best track for this role
RAG and Knowledge Systems
Chunking, embeddings, hybrid search, reranking and evaluation for retrieval-augmented generation, with the failure modes that show up once real users arrive.
Questions
Do I need vector database experience specifically?
You need search fundamentals. Vector search is one implementation. Understanding BM25, ranking, and why dense retrieval misses exact identifiers is what transfers, and it is covered in the RAG track.
Is this role mostly maintenance?
Less than people expect. Corpus growth, relevance regressions, and access control are continuous design problems rather than maintenance tickets, especially as a product grows.