Gen AI Developer

Exploratory Data Analysis (EDA)

- At least one industrial project on preprocessing and EDA using Python/R

- Basic understanding of Statistical concepts like mean, median, variance, correlation, normalization, histograms, boxplots, linear regression

- Basic understanding of probability, conditional probability, sampling methods

- Must have experience in data visualization using Python/R

- Must have experience in extracting information using SQL or similar languages

Machine Learning Model Development

- At least one end to end industrial project on classification or clustering using Python/R, which includes explanation of the business use case & data collection

- The project ideally should include hyperparameter tuning and comparing multiple algorithms and finally choosing one with proper justification.

- Good algorithmic understanding of Decision Trees, k-NN, Random Forest

- Decent understanding of Neural networks and deploying them at least locally, if not in the production environment (Good to have, not must).

- Must have Experience in at least one Cloud service like AWS, Azure or Google Cloud (Google Cloud Platform is highly preferred)

- Experience in MLOPs, Django, FLASK & Dockers etc. (Good to have, not must).

Libraries: scikit-learn, TensorFlow, keras, PyTorch, MLflow

Multimodal Generative AI

- At least one end to end industrial project on LLM tuning or RAG development or Synthetic data generation.

- Integration of multiple data types while developing RAG (Good to have, not must)

- Must have thorough understanding of Evaluation Metrics of LLM & RAG like BLEU, ROUGE & Cosine Similarity

- Good understanding of GANs, VAEs, RNNs, LSTMs, Attention & Transformers

- Hands on data preprocessing: Vector databases, chunking, Word Embeddings etc.

- Know-how of Zero-shot or few-shot inference, Chain of thought prompting, ReAct

- Understanding of AI Agents and hands on Tensorflow Serving, LangGraph, LangChain & Streamlit (Good to have, not must)

The below has been the area of focus

1. Good understanding of fundamentals of different machine learning algorithms.

2. Good communication skills and ability to explain in simple words is required as the candidate will be working across multiple teams

3. In-depth understanding of Neural Networks and LLMs.

4. Hands on Development of RAG models

5. Interest and eagerness to learn new things.

6. Understanding and Explaining of CNN architecture.

7. Exposure to ML algorithms by naming a few and explaining the same

8. Implementation of LLM / RAG

9. LLM architecture as the candidate will have to understand the same.