ML/AI Developer

Responsibilities: - Designing and developing machine learning systems, implementing appropriate ML algorithms, conducting experiments, and staying updated with the latest developments in the field. - Create data models, perform statistical analysis, and train and retrain systems to optimize performance. - build efficient self-learning applications and contribute to advancements in artificial intelligence. - Run machine learning tests and experiments - Implement appropriate ML algorithms - Use GPU for training, distributed computing pyspark, and parallel compute in libraries in python - Provide understanding of how components and processes work together and communicate with each other using library calls, REST APIs queueing/messaging systems and database queries - Provide system design to avoid bottlenecks to let algorithms scale well with increasing volumes of data Basic Qualifications: 5+ years of experience in the following: - PyTorch, NLTK, SciPy, Scikit-Learn, Numpy, OpenCV or equivalent for image preprocessing - SQL/NoSql databases and queries - One or more ML toolkits or Python frameworks - Deep Learning concepts - Apply standard implementations of machine learning algorithms effectively by choosing a suitable model such as decision tree, knn, neural net, or an ensemble of multiple models Nice to have: - Understanding of probability and statistics and machine learning concepts such as precision, recall, optimization, hyperparameter tuning, overfitting, and interpretability

- Coding best practices, OOD/OOP, modular design, SOA, and systems architecture Technical Skills: - Python, pyspark, or R programming language for coding - Kubernetes and docker for deployment - AWS sagemaker, or EC2 instances for cloud - MYSQL, Oracle, mongodb, or Redshift DB for database - Cloudera Distributed Platform for computing & deployment deep learning/neural networks packages like pytorch, tensorflow in python, and use GPU for training distributed computing pyspark and parallel compute in libraries in python