Amazon Senior Deep Learning Architect AWS Gen AI Innovation Center 

Mexico, Mexico City 

274829492

You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience.We’re looking for top architects, system and software engineers capable of using ML, Generative AI and other techniques to design, evangelize, implement and fine tune state-of-the-art solutions for never-before-solved problems.Key job responsibilitiesAnalyze and extract relevant information from large amounts of historical data to help automate and optimize key processes
Ensure the system is scalable and capable of handling large datasets and high-demand workloads to support Gen AI initiatives.A day in the life
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.Work/Life Balance
Sales, Marketing and Global Services (SMGS)

- Bachelor of Science degree in Computer Science, or related technical, math, or scientific field (or equivalent experience)
- At least 5 years of experience in designing, building, and/or operating large scale enterprise IT systems in a production environment
- At least 3 years of public cloud computing experience in AWS or other large scale cloud providers
- Relevant experience hosting and deploying ML solutions (e.g., for training, fine tuning, and inference)
- Experience coding in Python, R, Matlab, Java or other modern programming languages
- Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients

- Masters or PhD degree in computer science, or related technical, math, or scientific field
- Strong working knowledge of deep learning, machine learning, generative AI, and statistics
- Hands on experience building models with deep learning frameworks like MXNet, Tensorflow, Caffe, Torch, Theano or similar and deep learning (e.g., CNN, RNN, LSTM)
- The motivation to achieve results in a fast-paced environment