Mohd Ayaan Anwar

Software Engineer

Delhi, India3 yrs 3 mos experience

Most Likely To Switch

Key Highlights

Expert in Ethernet driver development for diverse SoCs.
Developed predictive ML models for disaster management.
Published research in federated learning and edge AI.

Stackforce AI infers this person is a skilled Software Engineer specializing in driver development and machine learning applications.

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Skills

Core Skills

Kernel DriversLinux KernelC (programming Language)LinuxMachine LearningPython (programming Language)

Other Skills

Linux Device DriverDevice DriversCommunicationDeep LearningKernel ProgrammingProblem SolvingCode ReviewPandas (Software)NumPyAmazon Web Services (AWS)Signal ProcessingFederated LearningBiomedical ResearchKerasGit

About

Engineer at Qualcomm working on Ethernet driver development across Router, IoT, and Automotive SoCs. Experienced in Linux kernel, QoS, hardware offloading, and upstream driver enablement. Previously developed predictive ML models for disaster management at AiDash, and published peer-reviewed research in federated learning and edge AI. Interested in systems programming, scalable network driver design, and Kernel hacking.

Experience

3 yrs 3 mos

Total Experience

1 yr 7 mos

Average Tenure

2 yrs 11 mos

Current Experience

Qualcomm

2 roles

Software Engineer

Promoted

Dec 2024 – Present · 1 yr 6 mos · Hyderabad, Telangana, India

Linux Device DriverDevice DriversKernel DriversLinux Kernel

Associate Software Engineer

Jul 2023 – Dec 2024 · 1 yr 5 mos · Hyderabad, Telangana, India

C (Programming Language)Linux

Aidash

Data Science Intern

Jan 2023 – Jun 2023 · 5 mos · Gurugram, Haryana, India

Contributed to the DDMS (Disaster & Disruption Management System) platform.
Delivered 7 new time-sensitive prediction modules for a major client.
Deployed statistical and machine learning models for predicting regional outages, storm direction, and affected customers using 10-year historical storm data.
Streamlined the storm outage model inference pipeline, reducing the notification time from 2 hours to 1 hour (50% decrease).
Developed pipelines for obtaining weather data for any new region using Shapely polygons and conversion of Apache Spark Dataframes to CSVs located in S3 buckets.

Machine LearningPython (Programming Language)