The client, an internal Amazon organization, was lacking proper validation of new features. This led to an inefficient deployment process and potential downtime for customers.
The client, an internal Amazon organization, was receiving irrelevant alerts, even when these had been previously identified as false positives. This caused inefficiencies in the transaction authorization process.
The client, an Amazon sub-organization, lacked upper-level approval pertaining to data-classification security threats. This led to the organization?s 2 most important applications being categorized as untrustworthy.
The clients, multiple AWS-internal organizations from the United States, Europe and India looking to screen transactions against denied parties were struggling with availability & latency problems, because of using a system only available on the US West Coast region.
The client?s (an Amazon internal regulator?s) screening engine had difficulties identifying (matching) postal codes within noisy input, making the end-to-end process omit transactions containing addresses of potential perpetrators.
The client, an Amazon-internal team, lacked insights into its timely deployment of denied parties lists. This misalignment made it difficult to track which list revisions were deployed and used in screening, how long the entire process took and maintaining legal compliance.
The client, an Asian-market leading automotive stakeholder, wished to surpass its competitors in terms of up-to-date navigation systems. They were facing issues with timely gathering street-level imagery updates from various sources and training & updating ML models for infotainment system maps.
2019 - 2022:
Computer Science
BA
Babe? Bolyai University of Cluj Napoca, Cluj-Napoca
ABOUT ME
The client, an internal Amazon organization, was lacking proper validation of new features. This led to an inefficient deployment process and potential downtime for customers.
The client, an internal Amazon organization, was receiving irrelevant alerts, even when these had been previously identified as false positives. This caused inefficiencies in the transaction authorization process.
The client, an Amazon sub-organization, lacked upper-level approval pertaining to data-classification security threats. This led to the organization?s 2 most important applications being categorized as untrustworthy.
The clients, multiple AWS-internal organizations from the United States, Europe and India looking to screen transactions against denied parties were struggling with availability & latency problems, because of using a system only available on the US West Coast region.
The client?s (an Amazon internal regulator?s) screening engine had difficulties identifying (matching) postal codes within noisy input, making the end-to-end process omit transactions containing addresses of potential perpetrators.
The client, an Amazon-internal team, lacked insights into its timely deployment of denied parties lists. This misalignment made it difficult to track which list revisions were deployed and used in screening, how long the entire process took and maintaining legal compliance.
The client, an Asian-market leading automotive stakeholder, wished to surpass its competitors in terms of up-to-date navigation systems. They were facing issues with timely gathering street-level imagery updates from various sources and training & updating ML models for infotainment system maps.
2019 - 2022:
Computer Science
BA
Babe? Bolyai University of Cluj Napoca, Cluj-Napoca
ABOUT ME