Professional Experience

Verses Inc.

Sr. ML Research Engineer - Contractor, Remote | Dec 2023 - Present

Details
  • Designed and deployed an edge-ready 3D perception stack for reliable warehouse object detection and depth-aware navigation on Jetson-class hardware.
  • Developed an YOLOv10n perception pipeline for warehouse-specific objects using a BlenderProc2-curated synthetic dataset, improving mAP from 0.15 to 0.62 at 640×640 and reducing the sim-to-real gap by retraining the synthetic-initialized model on cleaned, real warehouse data to reach 0.58 mAP
  • Optimized the end-to-end detection stack (pre/post-processing + YOLOv10n inference) with TensorRT on Jetson Orin NX and desktop GPUs, achieving 26 ms latency at 640×640 for real-time edge deployment
  • Integrated a DepthAnythingv2-based depth estimation module into the TensorRT perception pipeline, cutting depth latency from 1 s to 50 ms and delivering a combined depth + detection stack at 10 FPS (640×640) and 13 FPS (378×378) on Jetson Orin NX

Verses Global Bv

ML Tech Lead (Project: dAIEdge) - Contractor, Remote | Dec 2023 - Present

Details
  • Led development of an Active Inference–based routing agent leveraging the perception pipeline to perform obstacle avoidance, optimizing the planning stack and reducing planning time from 7 minutes to 21 seconds
  • Developed a PyMDP-based saccading agent using a Tapo security camera for active visual exploration, enabling person detection and tracking and later forming the basis of a peer-reviewed conference publication
  • Designed and maintained a 3D simulation pipeline in NVIDIA Isaac Omniverse to develop and evaluate routing agents in realistic warehouse environments before real-world deployment Publication: Towards smart and adaptive agents for active sensing on edge devices, D. Vyas, M. De Prado and T. Verbelen, HiPeac 2025 Link

Machine Learning Engineer

TerraLoupe GmbH - Munich, Germany | Oct 2019 – Apr 2020

Details
  • My responsibility involved improving toolchains for deep learning experiments to enhance their reproducibility and trackability.
  • Sourced, Cleaned and Automated MLOps pipeline for large-scale aerial image segmentation dataset
  • Integrated ML experimentation tracking using Sacred and Omniboard
  • Experimented with Deeplab(Semantic Segmentation Model) migration from GPUs to TPUs

ADAS Engineer

KPIT Technologies GmbH - Munich, Germany | Jan 2019 – Aug 2019

Details
  • My task was to create a Vehicle State Monitor that handles high-frequency data from multiple sensors on the car • Developed a GUI dashboard that can process processes high-frequency data to monitor and visualise the real-time health of vehicles
  • Designed the testing module for the Test Driven Development(TDD) of the Vehicle State Monitor (VSM)
  • Contributed to coding standards, code reviews, and source control management

Software Engineer

CNRS (XLIM Lab) - Poitiers, France | Jan 2017 – Aug 2018

Details
  • My responsibility was to integrate and optimize an algorithm for a simulator that visualizes radio wave propagation.
  • Optimized run-times by approx. 30% and streamlining system reliability Publication: CupCarbon: A new platform for the design, simulation and 2D/3D visualization of radio propagation and interferences in IoT networks

Education

M.Sc. Informatics

Technical University of Munich | Focus: ML and CV | Oct 2019 – Aug 2023

Bachelors of Technology in Computer Science

The LNM Institue of Information Technology, B.Tech Computer Science | Jul 2012 – Jun 2016

Skills

Languages
  • Python
  • C++
  • MATLAB
  • Java
  • bash

Libraries

Libraries
  • PyTorch
  • Tensorflow
  • ROS2
  • PyMDP
  • Jax
  • OpenCV
  • Numpy/CuPy

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