Vaibhav Goswami

Vaibhav Goswami

Specializing in AI solutions and cloud architectures. Passionate about transforming complex challenges into elegant, scalable solutions that drive innovation.

About Me

I'm Vaibhav Goswami, an AI and Cloud Engineer currently leading the development of the Generative AI platform at Thomson Reuters. With a background in Electronics and Instrumentation from BITS Pilani, I specialize in building scalable AI solutions and optimizing cloud architectures.

I am passionate about using Python, DevOps, Kubernetes, and cloud technologies to drive innovation and solve complex challenges. Outside work, I enjoy keeping up with the latest AI advancements and sharing insights with the tech community.

Technical Expertise

AI & ML

Generative AI Expert
MLOps Expert
TensorFlow Expert
Computer Vision Proficient

Cloud & DevOps

GCP Expert
AWS Expert
Terraform Proficient
Kubernetes Proficient

Development

Python Expert
SQL Intermediate

Professional Journey

Senior AI Software Engineer

Thomson Reuters

June 2022 - Present
  • Leading the development of the Generative AI Platform for the organization.

Senior Software Engineer

HSBC Technology

September 2021 - May 2022
  • Led cloud migration initiatives, managing the transition of data from on-premises to Google Cloud Platform.
  • Implemented automated Terraform pipelines, streamlining production deployments and reducing errors by 95%.
  • Developed Infrastructure as Code (IaC) for Compute Engines, Managed Instance Groups, Load Balancers, Cloud Storage Buckets, and BigQuery resources.

Software Engineer

HSBC Technology

July 2019 - August 2021
  • Developed an Automated Machine Learning (AutoML) Pipeline, automating 90% of the manual work in training and deploying models.
  • Designed and deployed a Python-based Automatic Test Data Generator for testing Dev and UAT pipelines, utilizing the Dash-Flask framework.
  • Created a Python Web API hosted on Google Cloud, enabling search functionality for over 50+ data assets within the organization.

Machine Learning Intern

UST Global

July 2018 - December 2018
  • Developed a Car Damage Detection Algorithm that estimates damage on individual car parts from images.
  • Created a Mask-RCNN model for object detection, segmenting car parts with 91% accuracy.
  • Built a multi-class image classification model to identify the severity of car damage (Minor, Moderate, Severe), achieving 83% accuracy in identifying damaged parts.

Let's Connect

If you'd like to discuss AI, cloud architecture, or simply want to connect, feel free to reach out!

Download Resume