Abhilash Panicker

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

• 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.

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

• 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.

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

• 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.

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

• 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.

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

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

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

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

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

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

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.