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• EAD (OPT/CPT/GC/H4)

• H1B Work Permit

• Corp-Corp

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• UG :- - Not Required

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• No of position :- ( 1 )

• Post :- 2nd Jan 2024

- 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