___Activities___• Developed technical expertise in Amazon SageMaker, a fully integrated Machine Learning Cloud Environment for Data Processing, Model Training, Model Deployment, Model Monitoring and features integrations such as Gen AI solutions from HuggingFace and LLM providers.• Worked with Strategic Enterprise customers to provide guidance and troubleshoot multiple issuesrelated to Amazon SageMaker, Amazon Bedrock, Dynamo DB, Amazon Personalize, AmazonTextract and Amazon Transcribe.• Worked with Technical Account Managers and Solutions Architects to define the best strategy ofthe ML use case implementation with AWS services.• Provided Mentoring and Support to colleagues and new joiners as part of a tenured team calledSupport Operations with on-call rotations.___Achievements___• 3rd place in the AWS SE internal Hackathon 2023 for North America region.• Published internal knowledge articles for SageMaker feature improvements.• AWS Certified Cloud Practitioner___Projects___These are some of the projects that I worked on as part of this professional experience. __ Model Deployment with SageMaker for different industries __• Helped customers to adopt, troubleshoot and deploy custom models on SageMaker Service toadd a competitive advantage to customer business.• Customized Deep Learning containers configuration, inference code and scaling policies fordifferent ML hosting instances (GPU and CPU).• Tested, fine-tuned and deployed Gen AI pre-trained LLM models such as Meta’s Llama andAnthropic’s Claude Models. Also, did the same with Hugging Face models and contributed.For example: https://huggingface.co/Salesforce/blip2-opt-2.7b/discussions/25__ ML Pipelines with SageMaker for different Industries __• Helped customers to adopt, build and troubleshoot End-to-end Machine Learning Pipelinesincluding Data Processing, Model Training, Model Evaluation, Model Monitoring. etc. Also,Implemented AWS Core Services to create more robust pipelines.

___Activities___• Python Scripts• SQL Scripts• Data Ingestion• Data Segmentation• Exploratory Data Analysis• Build predictive models with ML• Solution activation through main client's marketing services___Achievements___• Promoted to a regular Data Engineer from the Data Engineering Academy based on performance. • Treasure Data CDP professional certification.• IBM Data Science professional re-certification___ Projects _____CDP implementation for car industry__• Implemented a Data Science solution using Treasure Data CDP, developed python scripts forData Processing and configured Data Connectors for ingesting the customers data with therequired security approach. Finally, used the pre-built Machine Learning Model from the CDP forclients data unification and better segmentation.• Improved the click to sell conversion in 20% - 30% after the solution was released in productionenvironment.

In this journey I learned about Salesforce platform and the basics of Admin and Developer roles. We deployed a solution for a retail case of study and we applied the following tech stack: • Workflows• Data models• Process builder• Lightning web components• Visual Force• SOQL• User management

In this role I had the chance to get trained in Data Engineering and Software Development professional learning paths while I worked on activities as an IT recruiter. We applied Scrum framework to create recruiting pipelines in order to satisfy the critical demand of many IT profiles. • Create pipelines based on the account needs.• First contact & first screening.• Have meetings with the account leaders to present results.• Make strategies to find the best talent.@Mariana Peña was my manager and mentor. She also gave me the opportunity to start my Journey in IT sector and she was really interested in my professional growth when I was a junior for all the roles. I will be always grateful with Mariana, the best manager and the best example of a professional and human being in IT sector. When you read this again: Thanks a lot Mariana!