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.