Vikesh Pandey
Applied AI Engineer at xAI
London, England, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolVikesh Pandey is an Applied AI Engineer with over a decade of hands-on experience building ML and cloud-native solutions, and more than 15 years across machine learning, application development and DevOps practices. He has helped major financial institutions design and deploy generative AI and MLOps platforms that scale to hundreds or thousands of users while meeting stringent security, privacy and regulatory requirements. A former Principal GenAI/ML Specialist Solutions Architect at AWS and current xAI engineer and industry instructor, he blends deep software-engineering lineage—from desktop apps to microservices—with practical ML productionization. He is the author of "Generative AI for Financial Services," contributes to AWS SageMaker example notebooks, and frequently speaks and writes on applied AI topics, reflecting a strong commitment to knowledge sharing. Notably, his career demonstrates a rare mix of practitioner-level coding, cloud architecture, and domain-focused governance for regulated industries.
10 years of coding experience
13 years of employment as a software developer
Bachelor of Engineering (B.E.), Information Technology, Bachelor of Engineering (B.E.), Information Technology at Maharshi Dayanand University10+2, science(Non Med), 10+2, science(Non Med) at vmps
English
Stackoverflow
220reputation
37kreached
15answers
5questions
Github Skills (16)
amazon-sagemaker10
machine-learning10
jupyter-notebook10
aws10
python10
data-science9
deep-learning8
inference7
json6
xgboost6
maven6
ajax6
gson6
bounding-box6
amazon-web-services6
Programming languages (4)
Github contributions (5)
aws/amazon-sagemaker-examples
Jul 2020 - Aug 2021
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Role in this project:
ML Engineer
ML EngineerContributions:15 reviews, 6 commits, 12 PRs in 1 year
Contributions summary:Vikesh primarily focused on modifying and improving Jupyter notebooks related to machine learning models using Amazon SageMaker. Their commits involved correcting errors in S3 URLs within the code and updating the code to leverage Amazon SageMaker algorithms. They also made adjustments to notebook metadata, indicating a focus on the presentation and configuration of the machine learning examples. These modifications suggest that the user was involved in refining and ensuring the correct functioning of the provided machine learning examples.
pythonjupyter-notebooktrainingawssagemaker
vikeshpandey/data-structure-examples
Nov 2017 - Jul 2018
Contributions:16 commits, 37 pushes, 1 branch in 7 months
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