Marcilio Mendonca
Staff AI Forward Deployed Engineer at Google
Austin, Texas, United States
Summary
👤
Senior🎓
Top SchoolMarcilio Mendonca is a Staff AI Forward Deployed Engineer at Google Cloud with over three decades of software development experience and 16 years focused on enterprise cloud and ML engagements. He combines a PhD in Computer Science (AI + SE) from the University of Waterloo with hands-on delivery for Fortune 100 customers, specializing in AI agents, cloud infrastructure, and production-grade software engineering. Previously a senior solutions architect at AWS, he has a proven track record turning prototypes into business outcomes using platforms like Bedrock and SageMaker. Marcilio also bridges research and practice as the founder of S.P.L.O.T., a widely used research portal that applies automated reasoning to software configuration. Based in Austin, he pairs deep academic credentials and enterprise-scale delivery experience with creative pursuits—he’s a drummer and learning kitesurfer—reflecting a practical, curious approach to complex systems.
16 years of coding experience
21 years of employment as a software developer
BSc, Computer Science, Software Engineering, BSc, Computer Science, Software Engineering at Federal University of CearaPontifical Catholic University of Rio de JaneiroPhD, Computer Science, Software Engineering, PhD, Computer Science, Software Engineering at University of Waterloo
Github Skills (20)
aws-codepipeline10
data-api10
amazon-aurora10
aws-step-functions10
python9
state-machine9
aurora9
aws8
kubernetes8
mysql7
pgbouncer7
aws-cloudformation7
serverless7
jinja26
yaml-configuration6
Programming languages (6)
Github contributions (5)
aws-samples/aws-codepipeline-stepfunctions
Oct 2017 - Aug 2021
This project shows how to integrate AWS CodePipeline and AWS Step Functions state machines. The integration enables developers to build much simpler CodePipeline actions that perform a single task and to delegate the complexity of dealing with workflow-driven behavior associated with that task to a proper state machine engine. As such, developers will be able to build more intuitive pipelines and still being able to visualize and troubleshoot their pipeline actions in detail by examining the state machine execution logs.
Contributions:12 commits, 5 PRs, 11 pushes in 3 years 11 months
aws-codepipelineaws-step-functionsstate-machine
marcilio/splot
Jun 2010 - Oct 2015
Contributions:6 commits, 6 pushes in 5 years 4 months
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