ü  INDUSTRIAL IOT AUTOMATION, Embedded Development

ü  Machine LEARNING, DEEP LEARNING, COMPUTER VISION

ü  AGILE DEV: Scrum using JIRA

ü  Leadership and Mentoring experience

Architectural Patterns

Monolith, SOA, Microkernel , Microservices

Design Patterns

Publisher/Subscriber, Factory, Singleton

Data structures

Tries, Trees, Hash Tables, Heaps, Graphs, Linked Lists, Arrays

Prog. Languages

C++ with STL, C, Core Java

Build Systems

cmake, make, Jam, Jira, perforce, Git

OS

Linux, Windows, OpenWrt, Threadx

Machine Learning

Neural Networks, Computer vision

IOT

Zigbee, BLE, Wifi, Embedded Development, Edge, Cloud.

Experience Summary:

Lenovo: Gurgaon (August 2019 to present), Technical Architect (IOT)s

My Role

Ø  Technical Leadership and Major development in Machine Learning based Retail space Project – Automated checkout system that uses computer vision and ml to recognize objects being checked out

Ø  .Designed Cascading neural network to solve problem of complexity with this project.

Ø  Development on C++

Technology

C++ , IOT ,ML, Neural networks

Qualcomm: Noida (Sept 2010 to May 2019), India. Sr. Engineer

My Role

Ø  Senior architect and developer: independently delivered critical modules, also led teams

Ø  Worked on multiple languages – C++ with STL, Core Java 8, Python2, Shell Scripting, C#

Ø  Used various algorithms like Dijkstra’s/Prims, shape approximation etc.

Ø  Worked on Various OS – Linux, Windows, Embedded

Ø  Embedded Development, IOT based industrial automation

Ø  ML , Neural Networks on edge as well as on AWS

Ø  Agile Development, Jira.

Ø  Build /CI tools : make, cmake, scons

Technology keywords

C++, C, Algorithms, Java, Python, graphs, STL, trees, Linux, python, java, Json, xml, IOT (internet of things), Service oriented architecture, design patterns, ML, Neural networks

SDLC

Scrum using Jira tool in recent projects, Waterfall model in earlier projects

Projects and My role in them

Face Detection System Using Edge Devices  ( ML, Neural Networks, Computer vision)

·        System to mark attendance in Office automatically using face detection.

·        Machine Learning for neural network done on high end system with massive GPU, but learned network is to detect face on edge router devices.

·        Ensuring security of consumer data

Cloud based WIFI Network data analyzer with Machine Learning (ML)

·        Architected, Designed and co-developed the software part on the router in C++ using STL

o   Rest interface layer, Adaptation layer, WIFI adapter

o   Communication between layers using message queues, web sockets. MQTT also used.

·        Architected and independently developed from scratch in C#/C++/C a multi machine test automation system for automating testing.  Also Led team for further downstream implementation.

·        Designed, Co-developed the Cloud component that captures WIFI data provided by router.

·        Anomaly detection using ML(Machine Learning) on AWS Green grass

IOT project – C++, C, Linux, OpenWrt, Zigbee, Wi-Fi, BLE, C#., cloud component using Java 8, Rest

·        Independently made adaptation layer in C++ for openwrt/Linux. Also uses MQTT

·        Architected and Independently developed C++/C# based test automation system for multiple modules.

·        Cross compiled for Openwrt, Cross Compiled For embedded

·        ML- Anomaly Detection using Machine learning on AWS Green grass using Neural networks

Indoor Map – Java 7, JavaScript, graph algorithms, memory optimization

Cloud based indoor map server hosting multiple indoor maps, with client-side pages based on JavaScript that show indoor map overlaying over google map.

·        Developed map server hosting maps made in Java using SOA with rest calls to server which rest on AWS. Used AWS load balancing to scale out system and provide redundancy, reliability

·        Independently Created module that maps a walkable path in indoor map using Prim’s/Dijkstra’s

Indoor WIFI based location project - C++ with STL, data structures, algorithms, profilers

Server used to calculate location based on combination of relative intensity of WIFI signals, cellular location

·        Made in C++ on Linux, project uses STL data structures extensively – maps, ArrayLists etc.

·        Developed module providing altitude in position using barometry along with current weather data in C++