AI Video & Media Automation Pipelines

About this Gig

Short tagline: End-to-end pipelines for video ingestion, AI-driven transformation, and multilingual subtitle generation. Description: I build async, scalable media pipelines — HLS/MP4 ingestion with FFmpeg, LLM-powered translation across 30+ languages, S3-backed versioning, and containerized Celery workers. Production-tested at OTT scale, with ~95% processing time reductions and ~80% cost savings over naive implementations.

Requirements

1.Project goal — what you want the pipeline to do end-to-end. Examples: "auto-generate subtitles for new uploads," "translate existing English subtitles into 10 languages," "extract transcripts from HLS streams for search indexing." The clearer the use case, the tighter the pipeline. 2.Sample video files or stream URLs — 2–3 representative samples covering the formats you actually use (MP4, HLS .m3u8, MOV, etc.). Real samples are critical — they reveal codec issues, audio quality, and edge cases I need to handle upfront. 3.Source & target languages — which language(s) the audio is in, and the list of languages you need subtitles/translations in. If you need 30+ languages, let me know early so I can architect for parallel processing from day one. 4.Output requirements — subtitle format (SRT, VTT, ASS, burned-in), naming convention, where the final files should land (your S3 bucket, returned via API, dropped into your CMS), and any styling requirements (font, color, positioning). 5.Ingestion source — where videos come from. Examples: manual upload, S3 bucket trigger, CMS webhook, YouTube/Vimeo URLs, RTMP stream. If it's a CMS, share API docs or contact for whoever maintains it. 6.Scale & frequency — roughly how many videos per day/week, average length, and whether processing needs to be real-time or batch is fine. This drives worker count, concurrency limits, and cost estimates. 7.API keys & cloud access — credentials for your LLM provider (Gemini, OpenAI, Anthropic), AWS account (or wherever S3/storage lives), and any third-party services. Share securely via Bitwarden, 1Password, or your preferred method. 8.Deployment target — your AWS account, a VPS, or do you want me to set up infrastructure from scratch? If extending an existing system, repo access helps. 9.Budget for LLM/API costs — translation across 30 languages adds up. A monthly cost ceiling helps me pick the right model tier and batching strategy (e.g., Gemini Flash vs Pro for different stages). 10.Timeline & priority languages — your deadline and whether some languages should ship first (useful for phased rollouts).

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