Build and maintain
video ingestion, decoding, encoding, and transcoding
pipelines for VoD and Live systems.
Integrate
adaptive bitrate streaming
(HLS, DASH) into delivery pipelines.
Work with
FFmpeg, GStreamer, NVIDIA Video Codec SDK, and VAAPI
to implement efficient video processing components.
Ensure pipeline compatibility with multiple
codecs and containers
(H.264/H.265, AV1, VP9, MP4, MKV, TS).

Video Modification Modules

Implement
frame-accurate transformations
such as redaction (face/voice blurring), reframing, auto-zoom, and overlays.
Build timeline-aware components that align
scene metadata with video streams
for precise modifications.
Optimize GPU-accelerated filters for
real-time and batch processing
.

Performance & Scalability

Profile and tune pipelines for
low-latency live streaming
and
high-throughput VoD workflows
.
Contribute to
scaling strategies
for large video libraries and live event workloads.
Optimize for
cloud cost efficiency
while maintaining reliability.

Collaboration & Execution

Work with senior engineers to
translate designs into production components
.
Collaborate with AI teams to integrate
model outputs into video pipelines
(e.g., scene tagging, redaction cues).
Participate in
code reviews, testing, and deployment automation
.

Qualifications

Must-Have

2–5 years of experience in
video pipeline or multimedia systems engineering
.
Strong coding skills in
C++ and/or Python
.
Hands-on experience with
FFmpeg, GStreamer, libx264/x265, NVENC/DEC
.
Understanding of
video codecs and streaming protocols
(H.264/H.265, VP9, AV1, HLS, DASH, RTMP).
Familiarity with
GPU acceleration
(CUDA, NVENC/DEC, VAAPI, or equivalent).

Nice-to-Have

Exposure to
cloud-native deployments
(AWS/GCP/Azure, Docker, Kubernetes).
Experience in
real-time video editing or transformation pipelines
.
Familiarity with
timeline-based metadata, content retrieval, or AI-driven video modifications
.
Knowledge of
adaptive streaming and edge delivery optimizations
.

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