LLM Engineer
Prompt engineering, RAG, fine-tuning, LLM evaluation
Indicative CTC
18.2 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 the application layer: prompts, context assembly, and output contracts
- Own retrieval quality, and know whether a bad answer came from the retriever or the generator
- Write evaluations before optimising, so prompt changes are measured rather than guessed
- Keep token spend and latency inside budget as usage grows
What employers test
The skills that decide whether you clear the loop.
- Structured output and tool calling that actually parses
- Retrieval design: chunking, hybrid search, reranking
- An evaluation harness with a real test set
- Cost and latency reasoning
- Prompt injection defences
Salary bands
Indicative Indian market ranges for this role, not Avolute-AI graduate offers. Bands, not guarantees.
14-18 LPA
Common for backend engineers moving into LLM work
18-25 LPA
The band most candidates in India land in
25+ LPA
Product companies hiring specifically for LLM application work
How the program covers it
Where this role sits across the 12 months.
Months 1-3
LLM fundamentals and prompting that survives real requirements
Months 4-6
Structured output, provider abstraction, and the evaluation harness
Months 7-9
RAG in production, cost optimisation, guardrails
Months 10-12
A shipped LLM feature you can defend end to end
The interview loop
What each round is actually checking.
- 01
Screening
LLM experience and Python
- 02
DSA
Standard data structures
- 03
LLM deep-dive
Prompting, RAG design, evaluation strategy
- 04
System design
Designing a reliable LLM feature
- 05
Project deep-dive
Why you chose your retrieval approach
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
LLM Engineering
Prompting, context engineering, structured output, model APIs, and the evaluation and cost work that turns a working demo into something a team can ship.
Questions
Is prompt engineering still a real skill?
As a standalone job title, rarely. As part of the job, absolutely, and the harder part is context engineering and evaluation. That is most of what our LLM track covers.
How do companies assess LLM ability in interviews?
Usually a system design question with a model in it: design a support assistant and talk through retrieval, evaluation, cost and failure handling. They are testing judgement, not trivia.