Artificial Intelligence Architect

This is an IT support group
Singapore
SGD 60,000 - 80,000
Job description

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

Your Role

We are seeking an experienced AI Architect with a strong background in Machine Learning to join our team. The ideal candidate will have over 10 years of experience in AI/ML model development and deployment, along with deep dive analysis skills. This role also requires at least 3 years of experience as a Solution Architect.

  • Develop and deploy AI/ML models with a focus on deep dive analysis.
  • Implement end-to-end data-driven solutions using Analytical modeling techniques, machine learning, deep learning, and MLOps frameworks.
  • Demonstrate expertise in supervised and unsupervised machine learning, deep learning algorithms, and MLOps.
  • Conduct model validation, explainability, monitoring, and management (AI Glass Box).
  • Utilize latent semantic analysis, document similarity models, document classification, text analytics, and language models.
  • Develop recommender systems using content-based and collaborative filtering techniques.
  • Apply computer vision and image analytics, including neural networks, convolutional neural networks, and recurrent neural networks.
  • Perform Analytical modeling and forecasting analytics.
  • Conceive and implement Generative AI solutions.

Required Skills and Experience:

  • Over 10 years of experience in AI/ML model development and deployment.
  • At least 3 years of experience as a Solution Architect.
  • Proficiency in implementing data-driven solutions using statistical techniques, machine learning, deep learning, and MLOps frameworks.
  • Strong competencies in supervised and unsupervised machine learning, deep learning algorithms, and MLOps.
  • Experience with model validation, explainability, monitoring, and management (AI Glass Box).
  • Knowledge of latent semantic analysis, document similarity models, document classification, text analytics, and language models.
  • Expertise in recommender systems, computer vision, and image analytics.
  • Familiarity with neural networks, convolutional neural networks, and recurrent neural networks.
  • Experience in statistical modeling and predictive analytics.
  • Proven ability to conceptualize and implement Generative AI solutions.
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