SE - Python, AI

Overview

In-depth understanding of the Python software development stacks, ecosystems, frameworks and tools such as Numpy, Scipy, Pandas, Dask, spaCy, NLTK, sci-kit-learn and PyTorch.Experience with popular Python frameworks such as Django, Flask or Pyramid.Knowledge of data science and machine learning concepts and tools.A working understanding of cloud platforms such as AWS, Google Cloud or Azure.Contributions to open-source Python projects or active involvement in the Python community.Experience with front-end development using HTML, CSS, and JavaScript.Familiarity with database technologies such as SQL and NoSQL.Excellent problem-solving ability with solid communication and collaboration skills.Analyzing the ML algorithms that could be used to solve a given problemVerifying data quality, and/or ensuring it via data cleaningSupervising the data acquisition process if more data is neededFinding available datasets online that could be used for trainingDefining validation strategiesDefining the preprocessing or feature engineering to be done on a given datasetDefining data augmentation pipelinesTraining models and tuning their hyperparametersAnalyzing the errors of the model and designing strategies to overcome themDeploying models to productionProficiency with a deep learning frameworkProficiency with Python and basic libraries for machine learning such as scikit-learn and pandasExpertise in visualizing and manipulating big datasetsProficiency with OpenCVFamiliarity with LinuxAbility to select hardware to run an ML model with the required latency

Responsibilities

Responsible for designing, developing, automating, and maintaining software applications, participating in all phases of the software development lifecycle (SDLC).

Requirements

In-depth understanding of the Python software development stacks, ecosystems, frameworks and tools such as Numpy, Scipy, Pandas, Dask, spaCy, NLTK, sci-kit-learn and PyTorch.Experience with popular Python frameworks such as Django, Flask or Pyramid.Knowledge of data science and machine learning concepts and tools.A working understanding of cloud platforms such as AWS, Google Cloud or Azure.Contributions to open-source Python projects or active involvement in the Python community.Experience with front-end development using HTML, CSS, and JavaScript.Familiarity with database technologies such as SQL and NoSQL.Excellent problem-solving ability with solid communication and collaboration skills.Analyzing the ML algorithms that could be used to solve a given problemVerifying data quality, and/or ensuring it via data cleaningSupervising the data acquisition process if more data is neededFinding available datasets online that could be used for trainingDefining validation strategiesDefining the preprocessing or feature engineering to be done on a given datasetDefining data augmentation pipelinesTraining models and tuning their hyperparametersAnalyzing the errors of the model and designing strategies to overcome themDeploying models to productionProficiency with a deep learning frameworkProficiency with Python and basic libraries for machine learning such as scikit-learn and pandasExpertise in visualizing and manipulating big datasetsProficiency with OpenCVFamiliarity with LinuxAbility to select hardware to run an ML model with the required latency