Israel Almeida

Led various 3-4 people teams in different new product and R&D projects

• Developed, deployed and maintained deep learning models that create original abstract summaries from financial literature. using Huggingface python library and AWS Sagemaker. Optimized to work in several GPUs in parallel to increase summarization capacity of the service.
• Led the design and development for instagram-like social app for iOS and Android. Help direct vision for the social platform with content and marketing executives.
• Tech lead in development of customer support chatbot (Using mainly AWS Lex, Lambda and React).
• Tech lead for new education platform initiative. Worked closely with business and content teams to define the vision of the platform, how it will integrate with other products and coordinate development efforts of tech team. Platform is still in use today and scaled to millions of users.

• Own and run a software development agency. Have led various software projects from start (proposal) to finish (results delivered).

A cloud robot management platform to develop, deploy & operate smart robots at global scale. Some of my relevant accomplishments:

• Work with the core product team. Helped plan and develop new front end features and UIs. Introduced several UI components with React JS as well as fixing bugs to assist with the improvement of user experience.
• Manage and perform maintenance of multiple testing, staging and production environments on multi-cloud infrastructure (GCP, Azure, AWS) while keeping 0 downtime for the product.
• Lead regular release cycles coordinating QA and testing efforts in deployments in staging and production environments.
• Lead in the design, implementation and management of highly complex automated QA infrastructure. This QA framework included simulating robot fleets, full application environment with integration tests, large scale data processing and streaming etc. This became main defense line in catching unwanted bugs/regressions within minutes of being introduced with git push in the repo.

• Improved performance and reliability of databases, web services and other integrations and core AI recommender systems for the product.