Eduard Grigoryan
Yerevan, Armenia
Summary
🤩
Rockstar🎓
Top SchoolEduard Grigoryan is a full-stack developer with a decade of experience and over three years focused on building high-performance, scalable web applications using React/Next.js, GraphQL/Apollo, and Nest.js. He has driven measurable impact—like accelerating seller onboarding by 30% at Unisender—while optimizing SSR/ISR, SEO, and data layers with Prisma and PostgreSQL. At EPAM he contributed across frontend and backend boundaries, improving rendering performance, implementing secure JWT/Redis-based auth, and streamlining async workloads with BullMQ and Docker. Beyond web apps, he contributed to the notable Deeplake AI database—adding TensorFlow and GCS support and building dataset converters—demonstrating strengths in MLOps and large-scale data pipelines. A persistent problem-solver and fast learner, he pairs a formal background in nanotechnology and technological entrepreneurship with hands-on experience integrating cloud services and payment/third-party APIs.
10 years of coding experience
2 years of employment as a software developer
Master's degree, Technological Entrepreneurship, Master's degree, Technological Entrepreneurship at Moscow Institute of Physics and Technology (State University) (MIPT)Bachelor's degree, Nanotechnology, Bachelor's degree, Nanotechnology at Russian - Armenian University
Github Skills (10)
google-cloud-platform10
tensorflow10
data-pipelines10
data-pipeline10
python10
gcp10
data-storage9
machine-learning8
ml8
database-design8
Programming languages (6)
Github contributions (5)
activeloopai/deeplake
Nov 2019 - Jan 2021
Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
Role in this project:
Back-end Developer & MLOps Engineer
Back-end Developer & MLOps EngineerContributions:36 reviews, 180 commits, 43 PRs in 1 year 2 months
Contributions summary:Eduard primarily focused on integrating TensorFlow and GCP support into the Deeplake database for AI data. Their work included implementing TensorFlow dataset classes, developing storage solutions for Google Cloud Storage (GCS), and incorporating compression for data storage, likely to optimize data handling for AI applications. Furthermore, the user built a Waymo dataset converter to facilitate the ingestion of the Waymo dataset, showcasing expertise in data preparation and pipeline development for machine learning workflows.
pythonreal-time-datadata-streamjupyter-notebookvisualize
edogrigqv2/sudokuparser
Aug 2018 - Aug 2020
Contributions:10 pushes, 1 branch, 1 issue in 2 years
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