Mragank Yadav

CTO

Seattle, Washington, United States10 yrs 2 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Expert in designing scalable microservices architectures.
Proven leadership in technical team management.
Strong background in cloud services and data privacy.

Stackforce AI infers this person is a Backend-heavy Fullstack Engineer with expertise in cloud services and microservices architecture.

Contact

mragank2306@gmail.com LinkedIn

Skills

Core Skills

Distributed SystemsTechnical LeadershipMicroservicesTeam ManagementCloud ComputingMachine LearningJavaApi Development

Other Skills

.NET FrameworkAIMLAlgorithmsAmazon EKSAmazon Web Services (AWS)Artificial Intelligence (AI)Azure Cosmos DBC#C++CeleryCloud ServicesCoaching & MentoringComputer ScienceContinuous Integration and Continuous Delivery (CI/CD)Data Mining

About

Designed and developed multiple globally distributed, highly scalable microservices. Experience with NoSql document databases(Cosmos/Dynamo/Mongo), serverless architectures, elastic search indexes, microservice architectures(Rest/GRPC), redis/memcache, cloud services(Aws/Azure), event-based systems and data aggregation in highly concurrent environments. Solving problems like near real-time performance in eventually consistent systems, performance optimizations for a global audience, server and cluster management, CI/CD systems and Kubernetes based infrastructure.

Experience

Apple

Technical Lead

Sep 2022 – Present · 3 yrs 6 mos · Seattle, Washington, United States

Distributed SystemsData PrivacyCoaching & MentoringPrivacyArtificial Intelligence (AI)Technical Vision+4

Flexport

Senior Software Engineer

Jan 2021 – Sep 2022 · 1 yr 8 mos · Bellevue, Washington, United States

Team ManagementMicroservicesDockerDistributed SystemsCoaching & MentoringKotlin+2

Microsoft

Software Engineer ll

May 2018 – Jan 2021 · 2 yrs 8 mos · Redmond, WA, United States

Globally distributed and highly scalable Micro-services in C# and .net, NoSql document databases, serverless architectures, elastic search indexes, redis/memcache, azure cloud, event based data aggregation systems in highly concurrent environments and eventually consistent pipelines.

MicroservicesDistributed SystemsMicrosoft Azure.NET FrameworkC#Cloud Computing

Amazon

Software Engineering Intern

May 2017 – Aug 2017 · 3 mos · Seattle, Washington

Machine Learning Model Analysis And Visualisation :
Analyse Amazon's recommendation models (Personalisation) and develop visualisation tool that can help detect ranking and recommendations inconsistencies.
Worked on Java in the recommendation service and developed a website to detect any inconsistencies.

JavaData PipelinesMachine Learning