Delhi-NCR AI Engineer Skills Stack:
- Python (clean, modular, testable code)
- Math basics: linear algebra, probability, stats
- Classical ML: regression, trees, clustering, metrics
- Deep learning concepts + at least one framework
- Data handling: pipelines, validation, feature work
- Communication: explain model choices and trade-offs

What Interviews Probe:
- Can you choose the right model for a problem?
- How do you evaluate and avoid data leakage?
- Can you debug training issues?
- Can you ship a small service around a model?
- Do you understand LLM limits and evaluation?

Practical Tool Stack for NCR AI Roles:
- Core: Python, Git, Jupyter / VS Code
- ML: scikit-learn, PyTorch (preferred in many teams)
- NLP/LLM: Hugging Face Transformers, embeddings, RAG tools
- Serving: FastAPI, Docker
- Tracking: MLflow or simple experiment logs
- Cloud (basic): S3 / GCS, simple compute instances

Prioritisation Order:
1. Python + one solid DL framework (PyTorch recommended)
2. Classical ML + evaluation discipline
3. Hugging Face + one RAG / LLM project
4. FastAPI + Docker for deploy proof
5. Cloud and MLOps only after models work end-to-end
Depth in fewer tools beats shallow knowledge of many.

Every Strong AI Project Should Cover:
1. Business / problem statement
2. Data source and preprocessing
3. Model choice and why
4. Metrics and evaluation (not only accuracy)
5. Limitations and failure modes
6. How to run (README) + optional API / demo
GitHub clarity matters as much as the model itself.

High-Signal Project Ideas for NCR:
- Churn or lead scoring model with clear metrics
- Image / text classifier with error analysis
- RAG chatbot over company docs with evaluation
- Fine-tuned small model for a domain task
- FastAPI service wrapping a model + Docker
Pick 2–3 deep projects over 10 shallow ones.

LLM / Gen AI Checklist for AI Engineers:
- Build at least one RAG pipeline end-to-end
- Measure retrieval and answer quality
- Know when not to use an LLM
- Document prompts, evaluation and costs
- Understand basic safety and hallucination risks
This is more valuable than claiming "expert in Gen AI".

Resume / Portfolio Signals:
Skills: Hugging Face, RAG, prompt evaluation, FastAPI
Project: "Built RAG assistant over domain docs;
 improved grounded answers by X% on internal test set"
Link: working demo or clear GitHub README
Keep claims specific and measurable.

AI Engineer NCR Final Review:

Skills:
- Can explain ML/DL choices without slides
- Have clear evaluation discipline

Tools:
- One main framework (prefer PyTorch)
- Hugging Face + basic serving stack

Projects:
- 2–3 deep projects, not 10 shallow ones
- Every project has README, metrics, limitations

Target:
- Resume matches JD keywords honestly
- Portfolio opens and runs
- You can defend every project in interview

Simple 90-Day Plan:
Days 1–30: Python + ML + one strong classical project
Days 31–60: Deep learning + one DL project + GitHub polish
Days 61–90: LLM/RAG project + FastAPI/Docker + applications
Apply in parallel from day 60 with targeted NCR roles.
Consistency beats last-minute tool hopping.