Home Insights All authors Naresh Rajendra Shah
Naresh Rajendra Shah

Naresh Rajendra Shah

Machine Learning Architect

Naresh has over 8 years of development experience in the relatively nascent field of Deep Learning. He has worked with different types of solutions in the Deep Learning space from the days prior to Tensorflow and Pytorch where he used to build Deep Learning models on top of Theano/Caffe as frameworks. He is currently focused on nimble, agile solutions to accelerate value recognition of Generative AI solutions at Grid Dynamics. He has a Bachelor's in Electrical Engineering, BITS-Hyderabad, and a Master's in Business Analytics and Big Data, IE Business School. He has previously worked with different companies across fields like P&G, GE Healthcare, and his own startup, Untangle AI in the Explainable AI Space.


Check out the latest insights

Greyscale whale on digital background
White Paper
CTO insights: DeepSeek
Abstract, futuristic rendering of a human face merged with digital and network elements to represent agentic AI technology.
White Paper
CTO insights: Agentic AI
Agentic AI cover
White Paper
Agentic AI: The next evolution in enterprise automation
Futuristic glass hallway
Media & News
The CrowdStrike event: Addressing the mismatch between automated processes gone wrong and manual recovery
Driving business success with generative AI
White Paper
Driving business success with generative AI: Techniques for value-driven transformation
A book cover with the text: Generative AI in pharma and life sciences: pragmatic applications and outcomes
White Paper
Generative AI in pharma and life sciences: Pragmatic applications and outcomes

Get in touch

Let's connect! How can we reach you?

    Invalid phone format
    Submitting
    The CrowdStrike event: Addressing the mismatch between automated processes gone wrong and manual recovery

    Thank you!

    It is very important to be in touch with you.
    We will get back to you soon. Have a great day!

    check

    Something went wrong...

    There are possible difficulties with connection or other issues.
    Please try again after some time.

    Retry