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Rhino Federated Computing Tech Stack
Federated computing platform for secure, privacy-preserving AI across distributed data
Rhino Federated Computing builds infrastructure to train AI models across siloed datasets without moving raw data—a hard problem in regulated industries like healthcare and pharma. The stack is engineering-heavy (12 senior/principal engineers) and spans Python, Kubernetes, Terraform, and multi-cloud orchestration (AWS, GCP, Azure), with emerging LLM tooling (GPT, Gemini, LangChain). Active pain points around scaling platform infrastructure and automating complex data workflows signal they're past proof-of-concept and building for production deployments at enterprise scale.
Tech Stack 34 technologies
Core StackPython Django Flask FastAPI JavaScript TypeScript React Kubernetes Terraform Tailwind CSS Figma Express Cypress Playwright AWS Linux Ansible Prometheus Grafana Docker LangChain GCP Azure Bash Terragrunt VictoriaMetrics AIOps Llama GPT Gemini+4 more
What Rhino Federated Computing Is Building
◆Challenges
- Connecting siloed data
- Scaling federated computing platform
- Building category awareness
- Automating complex data workflows
- Improving privacy preservation
- Scaling platform infrastructure
- Ensuring secure data collaboration
- Customer deployment challenges
- Privacy and security controls
- Scaling brand
▲Active Projects
- Enhancing and scaling federated computing platform
- Federated training and inference pipelines
- Customer deployment programs
- Ongoing expansion initiatives
- Backend subsystem development from ideation to implementation
- Web-app for novel therapeutics discovery
- Web-app utilizing rhino federated computing platform
- Enhancing rhino fcp infrastructure
- Distributed deployment management
- Co-develop platform features
Hiring Activity
Steady20 roles · 6 in 30d
Department
Seniority
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