Savvysoft Technologies Pvt. Ltd. | Jobs | Python Developer – Machine Learning (H1B – FY22) | BigDataKB.com | 04-02-22

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Job Location: USA

Python Machine Learning Developers

Savvysoft is looking for professionals who are looking for new/fresh H1B visa for this year FY22

Savvysoft is seeking experienced Python developers with experience in writing and testing scalable Python Code and have good experience in Machine Learning tools to staff our current project needs in the US.

Responsibilities:

  • Develop and deploy machine learning services/applications
  • Develop front end interfaces and back-end services
  • Assisting development teams in defining application requirements
  • Implementing appropriate ML algorithms
  • Building reports for model monitoring and drive enhancements
  • Build end-to-end reusable pipelines from data acquisition to model output delivery
  • Design and build data infrastructure by partnering with data and production engineering teams.
  • Deliver quality ML models in a production environment
  • Involve in data extraction, cleaning, analysis and building reusable data pipelines
  • Customizing existing algorithms or develop new algorithms

Education:

  • Masters degree or PhD in Statistics, Mathematics, Computer Science, Engineering or Economics or other quantitative discipline (Bachelors degree with significant relevant experience will be considered)

Experience:

  • 6 9 years of demonstrated expertise in Python, with good knowledge of at least one Python web framework, such as Django, Dash, Flask including experience in Software Engineering Design and Development
  • Strong experience in designing and developing machine learning and deep learning systems
  • Strong experience in Knowledge Graph, Search Relevance and Ranking
  • Strong experience in software engineering principles, parallel and distributed computing, statistics, machine learning, cloud technologies like Azure, AWS, Google Cloud, IaaS, PaaS and SaaS

Skills Required: Good understanding of

Python framework and other libraries

Data science process, including data processing and analysis; feature extraction and engineering

Model training and evaluation

Common ML toolkits such as sci-kit learn and at least one deep learning framework (preferably PyTorch or Tensorflow)

Standard ML approaches and algorithms, including Linear Models, Decision Trees, Clustering, and Neural Networks

Algorithms – Logistic Regression, Random forest, XGBOOST, Markov Chain, model monitoring

Data structures, data modeling and software architecture

Breaking down complex or ambiguous problems into manageable tasks

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