AI/ML Engineers

Albert Kanda | Rohan Dhoj

Languages:- English,Hindi,Punjabi,French

Creative and innovative Artificial intelligence/ Machine Learning Engineer with over 4 years of experience in application design, development, testing, and deployment. Highly experienced in writing code and algorithms and building complex neural networks through various programming languages. Possess an unbridled passion for Artificial Intelligence with comprehensive knowledge of machine learning concepts and other related technologies.

CORE COMPETENCIES

Technical Expertise

Frameworks:, Pytorch, Scikit-learn, Xgboost,
Libraries: Pandas, SpaCy, NLTK, Google Chart API, Scikit-learn, NumPy, Matplotlib, PIL, OpenCV, Asyncio, SciPy, Ggplot2, Dash, Plotly, Streamlit, Gradio, Theano
Packages: CUDA, OpenCL
Neural Networks: Convolutional and Recurrent Neural Networks (LSTM, GRU, etc.), Autoencoders (VAE, DAE SAE, etc.), Generative Adversarial Networks (GANs), Deep Q-Network (DQN ), Feedforward Neural Network, Radial Basis Function Network, Modular Neural Network
DBMS: Apache, Pyspark, Kafka, AWS Infrastructure, Dask
Cloud: Microsoft Azure, AWS, Google Cloud
BI & Visualization: Kibana, SAS, Power BI, Amazon QuickSight, Qlik, Microsoft STrategy, Tableau

Knowledge Expertise

Mathematics: Matrices, Vectors, Derivatives, Integrals, Statistics (Mean, Standard Deviations, and Gaussian Distributions), Probability (Naive Bayes, Gaussian Mixture Models, and Hidden Markov Models)

Programming: Python, Python 3, SQL, JavaScript, C#, TypeScript, CoffeeScript, Bash

Big Data: Hadoop, Spark, Cassandra, MongoDB
Data Science: Acquisition, Preparation, Data Analysis, Data manipulation,
Machine Learning: Scikit learn, Supervised Learning, Unsupervised learning Reinforcement learning
Deep Learning: Tensorflow, Keras , neural Networks, CNN, RNN, GAN, LSTMs
Business Intelligence: Tableau, Qlikview, PowerBI
Signal Processing:
1. Solve problems using Signal Processing
2. Advanced Signal Processing Algorithms such as Wavelets, Shearlets, Curvelets, and Bandlets is a bonus.
Rapid prototyping: Launching products quickly in the market. The process is to develop a scale model and allows engineers to quickly develop a prototype and test it out.
Natural Language Processing (NLP): Libraries such as Gensim and NLTK.
Knowledge of algorithm theory to understand Gradient Descent, Convex Optimisation, Lagrange, Quadratic Programming, Partial Differential Equations, and Summations.
Neural network architectures: Used for coding tasks that are arduous for human effort, this has been extremely useful in areas such as translation, speech recognition, and image classification, and so on.

Work Experience

Bridging Model-Building and Production
1. Act as a bridge between the statistical & model-building work of data scientists & to build production-ready & robust AI/ML systems, platforms & services.
2. Combine AI/M knowledge with programming and software engineering skills to enable easier use of and access to said models and analyses
3. Translate the work of data scientists from environments such as python/R notebooks analytics applications, automating model training & evaluation processes.
Improving Systems: Responsible for developing machine learning algorithms to improve systems or processes by automating tasks
Improving Operational Efficiency
1. Exploring data, organising, cleaning, and analysing data to find patterns & attributes to build machine learning models.
2. Brainstorming on customer needs
3. Monitor and fine-tuning ML models to improving team productivity
Task-Oriented Machine Learning
1. Monitor, optimise, test, train, and deploy machine learning algorithms for specific tasks
2. Implement and carry ML-specific transformations, such as outlier detection, dimensionality reduction, feature engineering, missing value imputation, normalisation
3. Once the data is ready setting the training algorithm appropriately and executing it in a reasonable time to produce a satisfactory performance

Qualification

Bachelor of Science in Statistics