Senior Deep Learning Architect

hace 19 horas


Ciudad de México, Ciudad de México Amazon Web Services Mexico S. de R.L. de C.V. -... A tiempo completo
About the Role

We are seeking a highly skilled Senior Deep Learning Architect to join our team at Amazon Web Services Mexico S. de R.L. de C.V. -...

This is a unique opportunity to work at the forefront of Machine Learning and AI, applying cutting-edge Generative AI algorithms to solve real-world problems with significant impact.

Key Responsibilities
  1. Design and implement scalable cloud environments for customers to label data, build, train, tune, and deploy their models using ML and Generative AI tools.
  2. Collaborate with data scientists to create and fine-tune scalable ML, provide data labeling support, and evaluate workflows for Generative AI solutions.
  3. Interact with customers directly to understand business problems, help, and aid them in implementing their ML ecosystem.
  4. Analyze and extract relevant information from large amounts of historical data to automate and optimize key processes.
  5. Ensure the system is scalable and capable of handling large datasets and high-demand workloads to support Gen AI initiatives.
About the Team

Our team is a strategic group that helps AWS customers implement Generative AI solutions and realize transformational business opportunities.

We work closely with customers to imagine and scope use cases that create the greatest value for their businesses, select and train the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale.

What We Offer

We value work-life harmony and offer flexible work hours and arrangements to support our employees' well-being.

We also provide a range of benefits, including mentorship and career growth opportunities, to help our employees develop into better-rounded professionals.

Preferred Qualifications
  • Masters or PhD degree in computer science or a related field.
  • Strong working knowledge of deep learning, machine learning, and statistics, with hands-on experience building models with deep learning frameworks.
  • Hands-on experience with deep learning (e.g., CNN, RNN, LSTM).
  • Experience with statistical modeling and machine learning.


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