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ScribeShark — AI Transcription Platform
Built and deployed an AI-powered transcription platform for processing audio and video content. Implemented the backend and production infrastructure, including speech-to-text processing with faster-whisper, API services, containerized deployment, and reverse proxy configuration.
Designed the system to run AI workloads efficiently on self-managed infrastructure while keeping the architecture ready for GPU acceleration and scaling.
Key technologies: Python, FastAPI, faster-whisper, Docker, Nginx, Linux, AI/ML, speech recognition.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Built this fully automated AI video pipeline — from script to finished, published video, zero manual editing.
I mapped out and built an end-to-end n8n workflow that takes a topic and turns it into a complete video: AI writes the script, generates the voiceover, creates the visuals, auto-syncs captions, assembles everything, then stores and logs it automatically.
The hardest part wasn't any single step — it was getting all the tools to talk to each other cleanly. Each stage had to pass the right data to the next without breaking, so one failed generation wouldn't quietly kill the whole run. Lots of trial, error, and rebuilding connections until it just... worked.
End result: a system that can produce content on autopilot, at a scale a person doing it manually never could.
I build reliable, automated cloud systems that help teams deploy faster and operate with confidence.
As a DevOps Engineer, I work with:
AWS & Azure
Docker & Kubernetes
CI/CD with GitHub Actions
Infrastructure as Code with Terraform
Monitoring with Prometheus & Grafana
Logging with Loki and alerting with Alertmanager
Recently, I've been building projects around infrastructure automation, observability, Kubernetes operations, and AI-powered incident response.
My goal is simple: reduce manual work, improve system reliability, and help teams build better software delivery processes.
I am available for DevOps and cloud automation projects. If you're looking to automate deployments, improve your infrastructure, or set up monitoring for your applications, let's connect.
Built a PhysioCare AI Voice Receptionist designed to handle physiotherapy-related conversations through a natural voice interface.
The assistant can interact with users, understand their requests, and support common reception workflows for a physiotherapy service. The project focused on combining voice AI, LLM-based conversation, and backend/API workflows into a practical healthcare application.
Key areas included:
AI voice assistant
Conversational AI
LLM integration
Physiotherapy-focused workflows
Tool/function calling
API integration
Backend workflow development
Voice conversation flow and testing.
