Abhilash Panicker

Software Development Engineer | AI/ML Specialist
Bengaluru, IN.

About

Highly accomplished Software Development Engineer with extensive experience in designing, developing, and deploying large-scale, revenue-critical systems and advanced AI/ML solutions. Proven leader in building and scaling Automatic Speech Recognition (ASR) models and cloud-native applications, with a strong track record of driving significant business impact, improving system reliability, and mentoring engineering teams. Recognized for innovation, technical excellence, and a deep understanding of complex system architectures.

Work

AMAZON ADS
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Software Development Engineer II

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Summary

Contributed to the development and optimization of Amazon's Display Side Platform (DSP), focusing on revenue-critical systems and scalable web services.

Highlights

Designed and implemented multiple revenue-critical systems for Amazon's Display Side Platform (DSP), including high-traffic billing for Black Friday, Cyber Monday, and H1 Amazon Page, impacting $50M in annual revenue and generating $1.2M across North America, Japan, and Europe.

Led the successful migration of a critical system from a monolithic architecture to a scalable, modular AWS-based web service, resolving significant scaling issues and ensuring timely revenue ingestion for a $35B annual revenue stream.

Integrated OpenSearch for enhanced monitoring and performance tracking, deploying dynamic AWS AppConfig and resilient retry mechanisms to significantly improve system reliability and adaptability.

RUBRIK INDIA PRIVATE LIMITED
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Software Engineer

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Summary

Developed cloud-native protection solutions and cost optimization tools for multi-cloud environments.

Highlights

Ideated and developed from scratch a Cloud Native Protection Cost Calculator, providing cost optimization insights across AWS, Azure, and GCP clouds.

Developed, tested, and successfully launched a critical Cloud Native Protection feature specifically for AWS Gov Clouds, enhancing security and compliance.

SAMSUNG R&D INSTITUTE
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Lead Engineer

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Summary

Led the development and scaling of Automatic Speech Recognition (ASR) models for Samsung's voice assistant and mentored junior engineers.

Highlights

Designed a first-to-market proof of concept for custom keyword wake-up detection and verification, enhancing the functionality of Samsung's Voice Assistant Bixby.

Developed and launched End-to-End Deep Learning based Automatic Speech Recognition (ASR) models for Indian English, scaling to support over 1 million daily users.

Mentored and upskilled a team of 4 engineers, guiding their contributions to Indian English (In-En) and UK English (UK-En) Language Model Production.

SAMSUNG R&D INSTITUTE
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Senior Software Engineer

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Summary

Contributed to the development, launch, and optimization of Automatic Speech Recognition (ASR) packages for Samsung's Voice Assistant Bixby.

Highlights

Spearheaded the development, launch, and ongoing support of Automatic Speech Recognition (ASR) packages for UK English and Indian English for Samsung's Voice Assistant Bixby, launched in February 2019 and currently serving over 1 million users.

Optimized the production Language Model build pipeline by parallelizing processes, reducing average build times by 66% using Sun Grid Engine.

Education

IIT Bombay

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M.Tech.

Computer Science

Grade: GPA: 8.48

Awards

Best Project Implementation Award

Awarded By

Amazon

Awarded for the exceptional design and successful launch of a flat-rate billing system for Amazon DSP.

Samsung Citizen Award (Individual Category)

Awarded By

Samsung

Recognized for outstanding individual contributions and commitment during tenure at Samsung R&D Institute.

GATE 2016 Achievement

Awarded By

Graduate Aptitude Test in Engineering (GATE)

Achieved All India Rank 34 (99.97 percentile) among over 100,000 candidates in GATE 2016 Computer Science.

Publications

Leveraging Side Information for Transcription of Spoken Tutorials

Published by

IIT Bombay

Summary

Master's thesis focusing on improving Automatic Speech Recognition Systems through the integration of side information and an Attention-Based Encoder-Decoder model. Highlights: - Formulated and designed a system utilizing web text data and lecture slide data to enhance the accuracy of ASR for spoken-tutorial transcription. - Designed an Attention-Based Encoder-Decoder model specifically to address the complexities of code-switching in speech.

Languages

English

Fluent

Skills

Programming Languages

Python3, C++14, Golang.

Scripting

Bash, AWK, Sed.

Machine Learning Libraries

Tensorflow, Keras, Numpy, Matplotlib.

Web Technologies

HTML5, CSS3, Javascript, React, Next.js.

Tools & Systems

Git, Jenkins, AWS, Azure, GCP, OpenSearch, Sun Grid Engine.

Speech Recognition

Automatic Speech Recognition (ASR), Deep Learning, Keyword Spotting, Kaldi ASR Toolkit, Language Models.

Data Science & AI/ML

Convolutional Neural Networks (CNNs), DCNNs, VGG-Net, k-means clustering, Image Processing, Attention Models, Encoder-Decoder Models.

Projects

Keyword Spotting in Continuous Speech

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Summary

Developed advanced models for keyword spotting in continuous speech, leveraging established ASR toolkits.

Reverse Image Searching using DCNNs

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Summary

Designed and implemented a reverse image search system utilizing deep convolutional neural networks.

Artistic Style Transfer Using Convolutional Neural Networks

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Summary

Explored and implemented artistic style transfer techniques using advanced neural network architectures.