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Jitender Singh
Shekhawat

⚡ Chief Engineer, 6G Lab · Samsung R&D Bangalore

Research Engineer specializing in Reinforcement Learning, 6G/5G wireless systems, AI/ML model optimization, and high-performance computing. IIT Kanpur alumnus with 8+ years at Samsung R&D building next-generation telecom solutions.

8+
Years at Samsung
2
US Patents
IEEE
Published
IIT
Kanpur Alumni
JS

Jitender Singh Shekhawat

Chief Engineer · Samsung R&D Institute, Bangalore

Bengaluru, Karnataka, India
B.Tech EE — IIT Kanpur (2012–2016)
Samsung R&D Institute Bangalore
420+ LinkedIn Connections
6G/5GReinforcement Learning C/C++PyTorch CUDARTOS AI/MLLLVM
Open to new opportunities

About Jitender Singh

Research Engineer blending deep expertise in wireless systems with cutting-edge AI/ML.

I am a Research Engineer at Samsung R&D Bangalore, currently working as Chief Engineer in the 6G Lab, after completing my B.Tech in Electrical Engineering from IIT Kanpur.

My research interests span Reinforcement Learning, Machine Learning, Real-Time Operating Systems (RTOS), and fast data-path multicore design of protocols for high-throughput applications in 6G and 5G networks.

I have hands-on expertise in AI/ML model quantization (CNN & Transformers) for PyTorch, TensorFlow and ONNX deployment using QAT/PTQ strategies, work extensively with NVIDIA's PyAerial SDK with CUDA, and apply compiler optimizations like LLVM and LTO for HPC and low-latency systems.

AI/ML Research

Deep Q-Networks (DQN) & REINFORCE Policy Gradient for multi-objective 5G radio resource optimization.

Compiler & HPC

SIMD optimizations, LLVM/LTO for low-latency, high-throughput multicore parallel computing systems.

6G/5G Protocol Stack

RLC/PDCP layer development, baremetal programming, and distributed system design for next-gen networks.

8+
Years at Samsung
2
US Patents
IEEE
Published
IIT
Kanpur

🎓 Education

Indian Institute of Technology, Kanpur

Bachelor of Technology — Electrical Engineering

2012 – 2016

Kendriya Vidyalaya BSF Jodhpur

Higher Secondary Education

2006 – 2010

Work Experience

8+ years of continuous growth at Samsung R&D Institute Bangalore.

Chief Engineer
Mar 2022 – Present
Samsung R&D Institute India · Full-time
4 yrs 1 mo · Bengaluru (On-site)
  • 6G Lab — Testbed design for high-throughput and low-latency wireless systems
  • AI/ML model (CNN & Transformers) quantization for PyTorch, TensorFlow and ONNX using QAT/PTQ-based strategies
  • NVIDIA PyAerial SDK development with CUDA and Python Stack
  • 5G/6G micro-service based RAN stack development and architecture
  • SIMD, Compiler optimizations like LLVM, LTO etc. for HPC and low-latency systems
  • Tech stack: C/C++, Java, Python, Kubernetes, Docker, Baremetal, Distributed System Design
  • Multicore programming and baremetal programming expertise
C/C++CUDAPyTorch LLVM6GDockerKubernetes
Lead Engineer
Apr 2020 – Mar 2022
Samsung R&D Institute, Bangalore · Full-time
2 yrs · Bengaluru
  • Led advanced research in 5G Radio-Resource Allocation using Deep Reinforcement Learning
  • Architected high-performance protocol stack components for Samsung's 5G NR platform
  • Mentored junior engineers and drove technical strategy across projects
5G NRDeep RLC++Team Lead
Senior Software Engineer
Apr 2018 – Mar 2020
Samsung R&D Institute, Bangalore · Full-time
2 yrs · Bengaluru
  • Deep Reinforcement Learning research for Radio-Resource Allocation in 5G Networks
  • Implementation of DQN and REINFORCE Policy Gradient in libtorch C++/PyTorch for multi-objective optimization
  • Development of RLC layer for 5G New Radio (Rel. 15) Network side
  • Design and development of dynamic memory management to support large number of network users
DQNPyTorchC++5G NRRLC
Software Engineer
Jun 2016 – Mar 2018
Samsung R&D Institute, Bangalore · Full-time
1 yr 10 mos · Bengaluru
  • Design and Development of RLC and PDCP layer for pre-5G Trial CPE device
  • Multicore fast data-path design for the protocol stack; coding/debugging on RTOS
  • Performance testbed for evaluation of high-throughput protocol stack
  • Written Multicore Profiler on RTOS; MultiCore performance analysis for RLC/PDCP tasks to efficiently utilize parallelism
RTOSCRLC/PDCPMulticore

Research & Patents

IEEE-published research and two granted US patents in wireless telecom scheduling.

IEEE Publication · GLOBECOM 2020

A Reinforcement Learning Framework for QoS-Driven Radio Resource Scheduler

Published January 26, 2021. In cellular communication systems, radio resources are allocated to users by the MAC scheduler running at the base station. This paper presents a novel RL framework for QoS-aware scheduling in 5G networks.

View Publication
US Patent · Granted

Method and System for Radio Resource Scheduling in Telecommunication Network

A method for radio-resource scheduling in a telecommunication network providing optimized allocation for multiple quality-of-service classes simultaneously using reinforcement learning.

US11523411B2 · Dec 6, 2022
View Patent
US Patent · Granted

Method and Base Station for Managing Scheduling Performance of NGBR Bearers

A method for managing scheduling performance of bearers by a base station in a wireless network, enabling efficient management of non-guaranteed bitrate bearers in 5G New Radio.

US11523411B2 · Oct 1, 2022
View Patent
Research Interests

Current Focus Areas

Actively exploring cutting-edge areas at the intersection of AI/ML and wireless communication systems.

Reinforcement LearningMachine Learning 6G SystemsRTOS Fast Data-PathMulticore Design Model QuantizationRadio Resource Mgmt

Technical Skills

A comprehensive stack spanning systems programming, AI/ML, and cloud/DevOps.

Programming Languages

C / C++
Python
Java
Go
Shell/Bash

AI / ML Frameworks

PyTorch
TensorFlow
ONNX/CUDA
Deep RL
Quantization

Systems & HPC

RTOS
LLVM/LTO
Multicore
5G/6G NR
RLC/PDCP
SIMD

Cloud & DevOps

Docker
Kubernetes
Linux
Git

Licenses & Certifications

Greedy Algorithms, Minimum Spanning Trees & Dynamic Programming

Coursera · Dec 2018

Credential ID: WCGKXW657WGY

Show Credential ↗

Graph Search, Shortest Paths, and Data Structures

Coursera · Oct 2018

Credential ID: VCNYXHXJTFTH

Show Credential ↗

What Colleagues Say

"

Jitender has a strong command of reinforcement learning, having even published a research paper in the field. His depth of understanding extends across multiple programming languages, making him highly versatile in solving complex problems. His proficiency in CUDA programming enabled efficient parallel computing solutions, and his deep knowledge of MAC layer protocols made him an invaluable asset to our projects. His ability to break down complex problems and find optimized solutions is remarkable.

SK
Shivaji Kant
Google Innovator · HPC / AI & ML · Distributed Cloud Platform
"

His work in using reinforcement learning for packet distribution has been groundbreaking, showcasing a keen understanding of how to leverage ML to optimize systems and improve performance. Jitender's innovative approach and meticulous attention to detail have led to significant advancements. He is not only an exceptional professional but also a collaborative team player — highly recommended to anyone seeking expertise in telecommunications or reinforcement learning.

AS
Ankur Saxena
Staff Engineer at Qualcomm · Ex Samsung SRIB

Get In Touch

Open to research collaborations, 6G/AI projects, and new career opportunities.

Let's Build Something Extraordinary

Whether you're looking to collaborate on cutting-edge 6G research, AI/ML optimization projects, or explore new opportunities in high-performance systems — I'd love to connect.

Location
Bengaluru, Karnataka, India
Current Role
Chief Engineer, 6G Lab — Samsung R&D
Available for Collaborations

Send a Message