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Valério Cardoso
Staff Machine Learning Engineer @SecurityScorecard
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WORK HISTORY
Staff Machine Learning Engineer @SecurityScorecard
NY, US
Model Development & Optimization: Designed, trained, and optimized machine learning algorithms, enhancing model accuracy and efficiency through continuous research and experimentation with cutting-edge techniques.* Data Pipeline Engineering: Built and maintained scalable data pipelines for preprocessing, cleaning, and transforming large datasets to support model training and analysis.* ML Deployment & Monitoring: Deployed and managed scalable, high-performance machine learning models in production environments, while continuously monitoring and fine-tuning for optimal accuracy and robustness.* Cross-Functional Collaboration: Worked closely with data scientists, software engineers, and product managers to integrate ML solutions into products, ensuring alignment with project requirements.* Technical Leadership & Mentorship: Established and promoted best practices in machine learning, provided mentorship to team members, and created comprehensive documentation for models and processes.
EDUCATION
Universidade Federal do ABC
Bacharelado em Fisica, Sistemas Dinâmicos Complexos
Universidade Federal do ABC
Engenharia de Controle e Automação
Universidade Federal do ABC
Bacharelado em Ciências e Tecnologia, Santo Andre
ABOUT VALÉRIO CARDOSO
I’m passionate about understanding and solving complex problems, and this motivation guides my career to enter the field of machine learning. As a staff machine learning engineer, I was able to deliver value for many companies in challenge projects involving generative AI and “traditional” machine learning.I have almost a decade of experience in the field, part of which was dedicated to natural language processing projects. Especially in 2024, I successfully deployed complex agentic workflows and RAG systems for various industries.I am also a pragmatic person, with a strong sense of belonging to a group. I worked with a wide range of technologies, so I will highlight the most relevant to my current focus: Python, GCP, Azure, MLOPS, LLMOPS, Langgraph, Langchain, llama-index, Dspy, agentic workflows, inference frameworks for LLMs and machine learning, Kubernetes for machine learning workloads, and CUDA optimization
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