ARK Infotech Spectrum
Location
New York, NY | United States
Job description
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
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