Senior Python Engineer - PySpark Developer, Machine Learning

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Senior Python Engineer - PySpark Developer, Machine Learning

Hexaconcepts (India) Pvt. Ltd.

Bangalore INR 10 - 15 LPA Experience : 4 - 8 YRS. Openings: 5

Details:

The Client
They are the first and currently the only player, in the predictive engine-based self-service analytics product space for end business users. They have created the world’s first virtual data scientist, a product capable of understanding plain English queries from user, and autonomously being able to take decisions ranging from data selection to algorithm selection and finally visualisation and narratives, without any human intervention. And now they are embarking on bringing explainable component to our AI based solutions, to make the business decisions simpler, easier and adaptable to larger stakeholders.

Position: Senior Python Engineer - PySpark Developer, Machine Learning
Relevant Experience: 4yr - 6yr
Location : Bangalore
Joining: Immediate to 30 days (If your time to join is >30 days, resume may not be considered)

Min. Qualification:

Key skills (Must Have): PySpark, DASK, NoSQL, Docker, Machine Learning, Neural Network

a) 4+ years of experience in Object Oriented Python programming, PySpark
b) Strong experience in Distributed Processing (Cluster experience and Spark Job Deployment on cloud)
c) Experience in SQL and at least one NoSQL data store Elasticsearch, MongoDB, Cassandra, HBase
d) Experience in data processing services, Data pipeline/Glue, Lambda, ADF, Airflow
e) Experience with containerizing applications using Docker
f) Good in data analysis libraries Pandas, Numpy, Dask
g) Experience in Machine Learning libraries Scikit-learn, StatsModels, Spark ML
h) Knowledge in Deep Learning and Neural Network frameworks like PyTorch, Keras, TensorFlow

Skills Required:

PySpark, DASK, NoSQL, Docker, Machine Learning

Roles:

  • Build robust Machine Learning pipeline along with Data Engineer, Data Scientist
  • Integrate, optimize and maintain the machine learning models developed by the Data Scientist
  • Deployment, monitoring in staging, production pipelines
  • Apply generalization techniques for productization of the code to be used for different customers

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