Cloud Engineer

*About Marvedge *

Marvedge is an AI-powered SaaS platform that transforms how SaaS teams create
and share product demo videos. With built-in screen recording, intelligent focus
zooming, automated voiceovers, AI integrations, and real-time engagement
analytics, Marvedge enables creators to produce polished demo videos 5 faster.

Role Overview
We are looking for a Cloud Engineer to design, build, and scale the cloud
infrastructure supporting our AI-driven video processing and voiceover generation
engine. You will play a pivotal role in optimizing media storage pipelines,
managing serverless/containerized video rendering workloads (FFmpeg), and
deploying scalable AI features.

Key Responsibilities

● Cloud Infrastructure & Architecture: Design, deploy, and maintain scalable,
secure cloud infrastructure on AWS/GCP using Terraform or Infrastructure
as Code (IaC).
● Media Processing & Pipelines: Architect and optimize asynchronous
queues and background workers (e.g., BullMQ, FFmpeg) for
high-throughput video processing, transcoding, and rendering.
● Storage & CDN Optimization: Manage S3/Cloudflare R2 object storage,
lifecycle policies, and CDN distribution for fast, global video delivery.
● AI Feature Deployment: Build robust backend and cloud infrastructure to
serve AI models, voiceover generation(TTS) and automated captioning
workflows.
● DevOps & CI/CD: Establish automated CI/CD pipelines, container
orchestration (Docker/Kubernetes), and environment management for rapid
development cycles.
● Monitoring & Security: Implement logging, metrics, and alerting
(Prometheus, Grafana, Sentry) while ensuring security best practices,
access control, and cost optimization.

Requirements & Qualifications

● Experience: 1-4+years of professional experience in Cloud Engineering,
DevOps, or Infrastructure.
● Cloud Platforms: Hands-on expertise with AWS (EC2, S3, ECS/EKS,
Lambda, CloudFront) or GCP.
● Containers & Orchestration: Strong proficiency with Docker, container
management, and serverless architectures.
● Media Handling: Familiarity with video processing pipelines, FFmpeg,
background worker queues, and object storage.
● Tools & Languages: Experience with Terraform/Pulumi, Python,
Node.jS / TypeScript, and CI/CD tools (GitHub Actions, Jenkins).
● Mindset: Strong problem-solving skills, ownership mindset, and enthusiasm
for fast-paced SaaS startup environments.

Preferred Qualifications

● Experience with AI/ML model deployment (LLMs, TTS engines like
ElevenLabs/Whisper).
● Familiarity with HLS/DASH video streaming protocols.

Application & Next Steps

Interested candidates are invited to submit their resume, portfolio, or GitHub
profile. Qualified candidates will be scheduled for an initial technical screening
with the engineering lead.