Chile
Mathematician with strong backgound in machine learning, data engineering, computer science, and software engineering. I have 5+ years of relevant experience as ML Engineer and AI Engineer. Developing end to end data products, building data applications, and helping companies and teams within the companies to discover value in their data. As my main professional experience has been working at startups, I consider myself as data generalist or full-stack. This means I can work as Data Scientist, Data Engineer, Machine Learning Engineer or Data Analyst; since I have hands-on experience building end-to-end data products, from collecting raw data from the sources to productionize and deploy machine learning models. I enjoy a lot solving problems and looking for innovative solutions through data products and automating processes. Looking for opportunities to push my skills and keep learning to help the growth of the company through data driven solutions collaborating with cross-functional teams.
Working back to back with the CTO enabling all the data an AI infrastructure to power our product. Hybrid search engine, fine tuning language models, leveraging AI assisted product classification,
Developed a QA microservice API to perform RAG over customer's specific knowledge base to be used on customer's webpage as chatbot Developed document (HTML, PDF, Docx) indexing API using Python as a micro service, deployed in EKS cluster to parse docs into Vector DB (PostrgreSQL) to be used as knowledge base for Generative QA. Fine-tune transformers and LLM models (LLAMA 2, Gemini, GPT) for QA and summarization. Developed web scrapping microservice in Python deployed as application in Kubernetes EKS cluster.
End to end deployment of real time and batch risk (underwriting) models: data integration and prep, training, packaging, integration and monitoring as a microservice on a K8s cluster. Developed a data integrity framework on Scala to monitor sudden data changes on business metrics datasets.
Core contributor and Technical Lead migrating and standardizing peta-byte scale on-premise Data Lake to a cloud Data Lake house (S3, Snowflake), deploying Spark jobs in EKS cluster orchestrated with Airflow for batch ingestion (dozens of TB on daily basis)