Vigneshwaran .

Software Engineer

Navi Mumbai, Maharashtra, India2 yrs 10 mos experience

Key Highlights

Expert in building scalable backend systems.
Proven track record in real-time data processing.
Significant cost reductions through innovative solutions.

Stackforce AI infers this person is a Backend Engineer specializing in Ad Tech and Data Engineering.

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Skills

Core Skills

Distributed SystemsNode.jsApache KafkaOpenrestyKafkaData Pipelines

Other Skills

C++FFmpegSCTERedisRTPMPEG-TSFilebeatFlinkDruidBackend ArchitectureSystems DesignData StructuresAlgorithmsDockerStream Processing

About

Software Engineer focused on building scalable backend systems and real-time data pipelines. Experienced in developing high-performance solutions for video streaming and ad tech, with expertise in optimizing system performance and processing large-scale data. Skilled in Node.js, Kafka, Openresty and Redis. Passionate about solving complex challenges in distributed systems and data engineering.

Experience

2 yrs 10 mos

Total Experience

2 yrs 8 mos

Average Tenure

2 mos

Current Experience

Microsoft

Software Engineer

Feb 2026 – Present · 2 mos · Hyderabad · Hybrid

Azure Storage Team

Distributed SystemsC++

Jio platforms limited (jpl)

Software Engineer

Jun 2023 – Feb 2026 · 2 yrs 8 mos · Navi Mumbai, Maharashtra, India · On-site

◦ Architected and developed comprehensive HLD and LLD for an advertisement replacement system handling
Single Program Transport Stream (SPTS) over Real-time Transport Protocol (RTP) streams, enabling frame-accurate and seamless ad insertions without interrupting live playback. Gained practical experience working with MPEG Transport Stream (MPEG-TS) structures, RTP packetization mechanisms, and FFmpeg-based
transcoding/processing workflows, while extending the solution to support Multiple Program Transport Stream
(MPTS) over RTP for large-scale, multi-channel broadcast environments.
◦ Engineered an in-house HLS Stream Composer from the ground up using Node.js and FFmpeg, integrating
SCTE-35 markers for frame-accurate ad cueing. This custom solution reduced third-party encoding costs by 40% and provided greater flexibility for generating client-specific stream variants.
◦ Designed a high-performance OpenResty caching solution that reduced Redis calls by 40%, decreasing API latency by 42% (120ms to 70ms) and increasing throughput by 35%. Further optimized efficiency via buffered writes and compression, slashing latency another 30% and reducing server load under high concurrent traffic of 50K+ RPS.
◦ Designed a resilient logging pipeline to prevent data loss during outages: decoupled application performance by implementing asynchronous file writes with a Filebeat sidecar pattern for Kafka ingestion, and created custom backup scripts to guarantee zero data loss during Kafka downtime.
◦ Designed and developed a scalable real-time analytics platform using OpenResty, Kafka, redis, Flink and Druid.
The platform is designed to process processes millions of daily media player events to compute critical QoE metrics (rebuffering ratio, playback failure, smoothness) and audience engagement (watch time, geography) with
sub-second latency, with insights visualized through interactive Apache Superset dashboards that enabled
proactive monitoring and data-driven decisions.