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Full Stack Engineer - ML

No of Positions  No of Positions:   1

location Location: Bengaluru

date Tentative Start Date:   June 04, 2022

Work From Work From : Offsite

rate Rate : $ 12  -  20 (Hourly)

experience Experience : 5 to 8 Year

Job Applicants : 4
Job Views : 168
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Job Category : Information Technology & Services
Duration : 3-6  Month
Key Skills Required Skills
Big Data Machine Learning Tensorflow/PyTorch Pyhton Github
Description

Key Responsibilities:

• Explore big data from manufacturing and supply chains and identify unique business insights to drive critical decision making

• Develop machine learning (ML) and operations research (OR) algorithms and tools, such as regression, classification, deep learning, and optimization models for various manufacturing applications.

• Analyze large amounts of data to identify anomalies (pattern detection) and identify predictive signals.

• Deploy efficient and scalable ML solutions to the production system.

• Collaborate with cross-functional teams to apply and deploy machine learning to industrial problems.

Key Qualifications:

• 5+ years of hands-on experience in building analytics and machine learning algorithms to solve real-world problems

• 2+ years’ experience in productionalizing ML systems (MLOps)

• 2+ years’ experience in cloud-based ML dev and deployment (AWS)

• Strong programming skills with proficiency in Python and Github

• Experienced user of machine learning libraries such as sci-kit-learn, scipy, and Tensorflow/PyTorch

• Ability to explain and present machine learning concepts and results to a broad technical audience and executives.


Bonus Points:

• 2+ years’ experience in SQL and data modeling

• Knowledge of full-stack ML development such as Flask (backend), Bokeh, Holoviews, or Dash (front-end)

• Experience with Operations Research (OR) and nonlinear constrained or black-box optimization

• Experience in machine data (sensors, downtime, machine states, etc) for IoT & predictive maintenance applications

• Experience applying deep learning frameworks, such as PyTorch/ Torch, TensorFlow, and Keras to real-world applications

Education:

Master’s or Ph.D. degree in Computer Science, Math, Statistics, Physics, Engineering or related level of

experience required


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