Machine Learning Engineer TECHM-JOB-30814

Keylent, Inc.

$100K — $140K *
Information Technology
8 - 10 years of experience
Job Overview by Ladders

Qualifications

  • 10+ years of experience in machine learning and data analytics.
  • Strong proficiency in Python, with hands-on expertise in TensorFlow and NLP.
  • Experience in designing and developing complex BI and data analytical solutions.
  • Demonstrated ability to create large-scale data processing pipelines.
  • Strong knowledge of various machine learning methods including k-Nearest Neighbors and SVM.

Responsibilities

  • Design and develop machine learning systems and models.
  • Create and optimize large-scale data processing pipelines.
  • Collaborate with cross-functional teams to define project requirements.
  • Study and transform data science prototypes into production-ready solutions.
  • Select and prepare appropriate data sets for analysis.

Benefits

  • Onsite work from Day 1 in Atlanta, GA.
  • Exposure to advanced data science tools and technologies on AWS.
Full Job Description
Machine Learning Engineer TECHM-JOB-30814
Position :- Machine Learning Engineer
Location :- Atlanta GA (Day 1 onsite)

Preferably 10+ years' experience having strong Data Analytical Skills with Hands-on Experience in Designing and Developing complex BI and Data Analytical Solutions.
Preferred Experience with Client, Deep learning, TensorFlow, Python, NLP.
Creating large-scale data processing pipelines and build & train Client machine learning algorithms with tools like Python. Studying and transform data science prototypes.
Collaborating with cross-functional teams to define project requirements and scope.
Hands on experience with Data Science tools and technologies on AWS with Python.
Creating large-scale data processing pipelines to build and train Client machine learning algorithms.
Designing, developing and researching Machine Learning systems & models. Strong knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, Decision Forests.
Studying, transforming, and converting data science prototypes. Proficiency in handling imperfections in data and solving complex problems with multilayered data sets, and optimize existing machine learning libraries and frameworks.
Searching and selecting appropriate data sets before performing data collection and data modeling.

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