AI/ML Engineer
Core ML model development, training pipelines, MLOps
Indicative CTC
16.5 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.
- Train and fine-tune models on domain data, then measure whether the fine-tune was worth it
- Own the training pipeline: data prep, experiment tracking, reproducibility, and retraining cadence
- Ship models behind services and watch them in production rather than in a notebook
- Work with product engineers on latency and cost budgets when a model is too slow or too expensive
What employers test
The skills that decide whether you clear the loop.
- Python and PyTorch, comfortably
- How transformers actually work, not just how to call them
- Data pipelines and versioning
- Experiment tracking and reproducibility
- Enough MLOps to own a model in production
Salary bands
Indicative Indian market ranges for this role, not Avolute-AI graduate offers. Bands, not guarantees.
12-15 LPA
Strong campus offers, mostly product and tooling companies
15-20 LPA
The band most candidates in India land in
20+ LPA
Top product companies and research-adjacent teams
How the program covers it
Where this role sits across the 12 months.
Months 1-3
Python, DSA, and ML fundamentals, with a first deployed model
Months 4-6
Model training, fine-tuning decisions, and experiment tracking
Months 7-9
MLOps: serving, monitoring, and retraining pipelines in the cloud
Months 10-12
Mock system design and ML system design rounds
The interview loop
What each round is actually checking.
- 01
Screening
Python fluency and ML basics
- 02
DSA
Arrays, trees, DP, complexity
- 03
ML theory
Transformers, loss, optimisation, overfitting
- 04
System design
Designing a training and serving pipeline
- 05
Project deep-dive
Your capstone, defended line by line
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
Do I need a masters degree for this role?
Usually not in India. What matters is whether you can show trained models, a real pipeline, and you can defend your decisions in an interview. A strong project with clear reasoning beats the degree more often than not.
Is MLOps mostly Kubernetes?
Partly, but the harder parts are data versioning, reproducibility and deciding what to monitor. We cover all three, and only then the serving infrastructure, because most teams get that part from a platform team anyway.