AI/ML Engineer Technical Expertise

Frameworks:
PyTorch, Scikit-learn, XGBoost

Libraries:
Pandas, SpaCy, NLTK, Gensim, NumPy, Matplotlib, PIL, OpenCV, Asyncio, SciPy, ggplot2, Dash, Plotly, Streamlit, Gradio, Theano, Google Chart API

Packages:
CUDA, OpenCL

Neural Networks:
Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN – LSTM, GRU), Autoencoders (VAE, DAE, SAE), Generative Adversarial Networks (GANs), Deep Q-Networks (DQN), Feedforward Neural Networks, Radial Basis Function Networks, Modular Neural Networks

Big Data & DBMS:
Apache Hadoop, PySpark, Kafka, Cassandra, MongoDB, Dask

Cloud Platforms:
Microsoft Azure, AWS, Google Cloud Platform (GCP)

Business Intelligence & Visualization:
Tableau, Power BI, QlikView, Amazon QuickSight, SAS, Kibana, Microsoft Strategy

Mathematics & Statistics:
Matrices, Vectors, Derivatives, Integrals
Statistics: Mean, Standard Deviation, Gaussian Distributions
Probability: Naive Bayes, Gaussian Mixture Models (GMM), Hidden Markov Models (HMM)

Programming Languages:
Python (2 & 3), SQL, JavaScript, C#, TypeScript, CoffeeScript, Bash

Data Science:
Data Acquisition, Preparation, Analysis, and Manipulation

Machine Learning:
Supervised Learning, Unsupervised Learning, Reinforcement Learning

Deep Learning:
TensorFlow, Keras, Neural Networks, CNN, RNN, GANs, LSTMs

Signal Processing:
Problem-solving using signal processing techniques
Experience with wavelets, shearlets, curvelets, and bandlets is a bonus

Rapid Prototyping:
Experience with launching products quickly via scalable prototypes

Natural Language Processing (NLP):
Experience with libraries such as Gensim and NLTK

Algorithm Theory:
Solid understanding of Gradient Descent, Convex Optimization, Lagrangian methods, Quadratic Programming, Partial Differential Equations (PDEs), and Summations

Neural Network Architectures:
Application of complex neural architectures for tasks like translation, speech recognition, and image classification

Key Responsibilities

1. Collaborate with cross-functional teams to understand business requirements and identify AI/ML opportunities.

2. Design and develop ML models and algorithms to extract insights, make predictions, and automate processes.

3. Perform data preprocessing, feature engineering, and exploratory data analysis.

4. Train, validate, and fine-tune models using supervised, unsupervised, and reinforcement learning techniques.

5. Apply statistical and experimental analysis to evaluate and optimize model performance.

6. Deploy ML models into scalable, reliable, and maintainable production environments.

7. Work with software engineers to integrate AI/ML into new or existing applications.

8. Continuously monitor production models, identifying retraining or optimization needs.

9. Stay current with AI/ML research, tools, and frameworks, recommending adoption where applicable.

10. Collaborate with data engineers on scalable data pipelines and infrastructure.

11. Contribute to the development and maintenance of AI/ML infrastructure (model repositories, version control, etc.).

12. Provide mentorship to junior engineers and foster a culture of innovation and continuous improvement.

Trial Task

Please complete the following to demonstrate your skills:

1. Explain the bias-variance trade-off in machine learning.
   Describe how it affects model performance and the implications for underfitting vs. overfitting.

2. Explain ROC curves and AUC (Area Under the Curve) in binary classification.
   Include their interpretation and how they help in evaluating classification models.

3. Develop a clustering algorithm from scratch using a programming language of your choice.
   Apply the algorithm to a dataset of your choosing and provide a brief analysis of the clustering results.