Rishabh Shukla - Applied Science Lead at UnlikelyAI

Rishabh Shukla

Applied Science Lead at UnlikelyAI

London, England, United Kingdom

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Summary

👤

Senior

🎓

Top School

Rishabh Shukla is an Applied Science Lead based in London with 13 years of experience building and scaling AI systems across healthcare, safety, and fintech domains. He leads teams combining research and engineering to deliver grounded neuro-symbolic, RL-based, and LLM-driven solutions, having produced production-grade pipelines serving hundreds of thousands of daily requests. His background spans building epidemic monitoring and multi-omics drug-discovery platforms, large expert-annotated datasets for few-shot fine-tuning, and explainable toxicity/misinformation detectors, reflecting a strong blend of applied research and MLOps. Early open-source work includes building a Java batch API for low-connectivity regions in the MifosX project, showing a long-standing focus on practical, impact-driven engineering. Colleagues describe him as a research-minded leader who bridges academic collaborations and product teams to turn complex ML ideas into robust, cost-efficient systems.

13 years of coding experience

9 years of employment as a software developer

Bachelor of Technology (B.Tech.) Computer Science, Bachelor of Technology (B.Tech.) Computer Science at Shri Mata Vaishno Devi University

English, Hindi, Italian

Github Skills (17)

api-rest10

apidoc10

api-design10

restful-api10

java10

javas10

api10

spring-boot10

rest-api10

batch-processing10

integration-testing10

json9

finance9

bank9

banking9

Programming languages (5)

JavaRHTMLJupyter NotebookPython

Github contributions (5)

openMF/mifosx

Jun 2014 - Aug 2014

NOW DEPRECATED. All development occurs on Apache Fineract (https://github.com/apache/incubator-fineract).

Role in this project:

Back-end Developer

Contributions:8 commits in 1 month

Contributions summary:Rishabh contributed to the development of a batch API for processing multiple requests. They added features such as a `BatchApiResource` for handling batch requests, and strategies for handling different commands like createClient, updateClient, applyLoan, and applySavings. The contributions included integration tests to ensure the correct functionality of the batch API and its commands. The user also worked on dependency resolution and exception handling within the batch processing logic.

javafineractincubatorapache

rishy/data-tag

Nov 2014 - Dec 2014

Contributions:23 commits in 1 month

nlpnltkwikipediaopen-datatextual

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