Sr Software Engineer

• Designed and developed a scalable AI backend platform for real-time noise cancellation, where
audio streams were processed and enhanced using cloud-based AI models on AWS.
• Built FastAPI-based backend services to handle low-latency audio ingestion, preprocessing,
inference orchestration, and streaming responses to client applications.
• Implemented real-time audio streaming pipelines between desktop/mobile clients and backend AI
services using TCP/UDP, WebSockets, and custom socket-level protocols to meet strict latency
requirements.
• Integrated backend AI services with client-side SDKs to enable seamless noise suppression, echo
cancellation, and voice isolation using AI models.
• Executed performance-critical audio processing logic in C/C++, optimizing CPU utilization, memory
footprint, and end-to-end latency for live communication scenarios on client side.
• Designed asynchronous and parallel processing architectures (async I/O, worker pools, batching) to
scale AI inference workloads while maintaining audio quality and stability.

• Developed desktop audio manipulation tools enabling non-destructive trimming, looping,
normalization, channel mapping, and sample-accurate editing for professional sound libraries.
• Implemented high-performance audio file import/export pipelines supporting WAV, AIFF, FLAC, and
CAF formats, including batch conversion and sample-rate/bit-depth processing.
• Built timeline-based waveform visualization components with zoom, region selection, and marker
support to enable fast auditioning and precise sound editing.
• Designed and applied metadata-driven workflows allowing users to tag, reorganize, and restructure
large sound libraries while preserving embedded audio metadata.
• Improved background indexing and caching systems to handle very large audio collections
efficiently without blocking UI operations.
• Integrated external DAW workflows by implementing drag-and-drop, hot-key triggers, and direct
export pipelines to Pro Tools, Logic Pro, and other industry tools.
• Optimized file I/O, memory usage, and threading models for audio-heavy desktop applications on
macOS and Windows.
• Participated in full software development lifecycle activities including feature design,
implementation, refactoring, and long-term maintainability improvements.

Enhanced automation frameworks and custom tooling for OTT video-streaming and TV platform
applications using Python and C/C++, reducing manual QA effort and accelerating release cycles.
• Built and enhanced backend services with Django to support content ingestion, media validation,
metadata processing, WebRTC signaling and API integrations across streaming workflows.
• Developed scripts and services to validate transcoding outputs, real-time and on-demand
streaming pipelines, DRM packaging, and adaptive bitrate streaming formats (HLS/DASH)
across multiple devices based on Django based backend system.
• Implemented end-to-end streaming workflow solutions in collaboration with platform
engineers, covering content upload, encoding, packaging, delivery, and playback.
• Created performance and load-testing utilities using HP LoadRunner and custom scripts to
simulate high-concurrency streaming scenarios and analyze CDN behavior.
• Developed data validation and processing logic for media metadata, manifests, and APIs using
JSON, XML, and text formats with PostgreSQL on Linux and Windows environments.
• Actively contributed to Agile development practices, participating in sprint planning, design
reviews, code reviews, and CI/CD pipelines on docker to deliver scalable TV and streaming
platform solutions.