Megha Agarwal
AI ML Specialist at Google
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolMegha Agarwal is an AI/ML specialist with 13 years’ experience architecting and operationalizing cloud-native AI solutions for strategic enterprise clients across Azure and Google Cloud. She has led end-to-end AI/ML strategy—identifying high-impact use cases, building data science practices, and accelerating workloads from envisioning to production—most recently at Google after a senior architect role at Microsoft. Her background as a hands-on data scientist and backend contributor (including work on the Daemo crowdsourcing platform) gives her both product-facing and implementation depth. Trained in advanced computing and machine learning at the University of Bristol, she combines research-grade ML knowledge with pragmatic engineering to deliver measurable business value. An entrepreneurial streak traces back to founding a startup and early roles spanning speech engineering to fintech decision science, demonstrating versatility across domains.
13 years of coding experience
9 years of employment as a software developer
Bachelor of Technology (B.Tech.), Computer Engineering, 8.21/10, Bachelor of Technology (B.Tech.), Computer Engineering, 8.21/10 at Nirma Institute of Technology, Nirma UniversityMaster of Science (MSc), Advanced Computing ( Machine Learning, Data Mining & HPC), Master of Science (MSc), Advanced Computing ( Machine Learning, Data Mining & HPC) at University of Bristol
Hindi, English
Github Skills (11)
3d-models10
rest-api10
serializer10
data-models10
python10
machine-learning-models10
crowdsourcing10
django10
postgresql9
postgrest9
angular4
Programming languages (3)
Github contributions (5)
crowdresearch/daemo
May 2015 - Jul 2015
The Daemo crowdsourcing platform
Role in this project:
Back-end Developer
Back-end DeveloperContributions:22 commits, 18 PRs, 8 comments in 1 month
Contributions summary:Megha focused on implementing and refining backend functionalities within the Daemo crowdsourcing platform. Their contributions included creating scripts to generate dummy data, enhancing the worker serializer to serialize additional classes, and adding read-only fields. The user also updated the Worker REST API, incorporating qualifications and currency features, alongside making view set adjustments to support these changes.
pythonethereummaterial-designpostgresangular
agarwal22megha/generative-ai
Jun 2023 - Aug 2024
Sample code and notebooks for Generative AI on Google Cloud
Contributions:32 pushes, 8 branches in 1 year 2 months
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