Personal details

Ayush P. - Remote back-end developer

Ayush P.

Based in: đŸ‡©đŸ‡Ș Germany
Timezone: Berlin (UTC+2)

Summary

I am a Senior Backend Engineer at Tier Mobility SE based in Munich, Germany.

Before moving to Berlin, I was an Assistant Tech Lead at Adacta Fintech based in Slovenia spearheading the development of insurance solutions for Europe-based companies such as the Sava and DVA Insurance while honing my skills as a full-stack engineer.

Before plunging into software development, I was a Machine Learning Engineer at Zemanta, an Outbrain Company where my day-to-day work involved implementing and analyzing the real-time machine algorithms and integrating them with the bidder infrastructure.

Before moving to Slovenia, I worked as a Product Engineer at Sprinklr, India where I was involved in implementing the machine learning module in the data flow execution pipeline using Kafka, Spark, and Spring frameworks.

Work Experience

Assitant Tech Lead
Adacta Fintech | Sep 2020 - Jun 2022
C#
MySQL
JavaScript
CI/CD
GitLab CI/CD

Leading a team of software engineers to spearhead the development of insurance solutions for DVA Insurance.
Organizing sprint grooming, planning, and reflections for the team
Onboarding new members of the team

Tech stack: Javascript, C#, Gitlab CI/CD, Docker, SQL, Elastic Search, FinTech

Education

Indian Institute of Technology Kharagpur
Master's degree・Mathematics and Computer Science
Jul 2012 - Jun 2017

Personal Projects

Machine learning tools for classification of qualitative traits of differential equation solutions IconOpenNewWindows
2017
Git
Machine Learning
Statistics
Mathematics
Julia
Implemented weighted non-linear regression, regularization, two-stage, and multiple shooting techniques to estimate the parameters of differential equations which has its applications in HIV-AIDS viral dynamics study. Wrote API to estimate the probability distribution of the parameters using Stan without having to write the stan code.
Support for Optimization with Complex Numbers in Convex.jlIconOpenNewWindows
2016
Git
Mathematics
Julia
Implemented support for optimization with complex variables in Convex.jl making it the first open-source package to support the above functionality and has many applications in quantum physics and AC power circuit optimization. Wrote the sample tests, documentation, examples and blog entries to encourage optimization community to use Convex.jl.