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  • Generative AI - ML Engineer

Industry Insurance
Location Karnataka Bangalore
Experience Range 3 - 8 Years
Qualification Post Graduate Diploma (PG Diploma)
Open

Functional IT Software-Other
Job Description
About Us
“Quess IT Staffing is India’s largest IT staffing company with over 20 years of experience in staffing IT professionals in 300+ companies across levels and skillsets. Our 10,000+ associates deployed in 80+ cities and towns are proficient in over 500 technological skills. Our associates help enable cutting edge solutions some of the biggest names across industried. Quess IT Staffing is a division of Quess Corp Limited, India’s leading business services provider and largest domestic private sector employer. Quess Corp Limited is - ‘A Great Place to Work’ certified – a testament to our excellent culture, people, and processes.”
About Company
https://itstaffing.quesscorp.com/
Roles and Responsibility

 Job title Generative AI - ML Engineer

Experience: 3-8 Years

Notice period: Immediate to 15 days

Location: Bangalore

JD:

Responsibilities:

·       Model Development:

·       Design, develop, and optimize generative machine learning models using frameworks such as TensorFlow, PyTorch, or Keras.

·       Experiment with various architectures including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and Transformer models.

·       Data Preprocessing and Feature Engineering:

·       Preprocess and clean large-scale datasets for training generative models.

·       Perform feature engineering and extraction to enhance model performance and accuracy.

·       Training and Evaluation:

·       Train machine learning models using labeled and unlabeled data, and evaluate their performance using metrics such as accuracy, precision, recall, and F1-score.

·       Fine-tune models, conduct hyperparameter optimization, and implement regularization techniques.

·       Model Deployment and Integration:

·       Deploy trained models into production environments and integrate them into existing systems or applications.

·       Ensure scalability, reliability, and efficiency of deployed models through monitoring and optimization.

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