Arango Tech Stack

Multi-model database platform for enterprise AI and context management

Software Development San Francisco, CA 51–200 employees Privately Held

Arango combines a multi-model database (graph, vector, document, key-value) with automated data pipelines and LLM integrations to build what they call a System of Context. The stack reveals both breadth and depth: core infrastructure (Kubernetes, Docker, Terraform, Ansible), ML tooling (MLflow, Triton, vLLM, TensorRT-LLM), and AI frameworks. Hiring is sales-led (9 roles) paired with engineering (8), skewed heavily toward senior talent, and the active projects (agentic workflows, Kubernetes hardening, CI/CD automation) point to an organization scaling both the platform and its operational maturity.

Tech Stack 26 technologies

Core StackAWS Python Docker Kubernetes Jenkins CircleCI Terraform Ansible GitHub Go Helm MLflow Salesforce ArangoDB SIEM AWS WAF GCP OpenAPI Swagger Hugo Triton SQL vLLM TensorRT-LLM NVIDIA

AdoptingRAG

What Arango Is Building

◆Challenges

Adoption across enterprise organizations
Cyber threat protection
Vulnerability remediation
Security monitoring
Robust lifecycle management of arangodb
Scaling and backup of stateful systems
High availability of arangodb on kubernetes
Maintaining ai infrastructure
Debugging complex production issues
Deploying complex ai platform

▲Active Projects

Kubernetes environment deployments
Python-based features for arangodb’s unified platform
Model fine-tuning and inference optimization
Managed service security tool implementation
Security monitoring and alerting
Kubernetes cluster hardening
Ci/cd pipeline automation
Iac infrastructure management
Agentic workflows for autonomous agents in distributed environments
Documentation for ai suite, data platform, arangodb core

Hiring Activity

Accelerating25 roles · 15 in 30d

Department

Sales

Engineering

Marketing

Security

Seniority

Senior

Junior

Mid

Principal

Intern

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About Arango

Arango builds an AI data platform designed to unify enterprise data in a form that LLMs can consume effectively. The core product is a massively scalable multi-model database that handles graph, vector, document, and key-value data simultaneously, with full-text, geospatial, and vector search built in. The broader platform includes automated data pipelines, multimodal data ingestion, LLM integrations, and agentic frameworks for context-aware retrieval-augmented generation (both graph and hybrid approaches). The company operates at a scale that requires significant operational infrastructure: Kubernetes orchestration, security monitoring, stateful system backup, and high-availability deployments are active pain points. Founded and based in San Francisco with 51–200 employees, Arango is a member of the NVIDIA Inception Program and AWS ISV Accelerate Program.

HeadquartersSan Francisco, CA

Company Size51–200 employees

Hiring MarketsGermany, Poland, United States, France, India, United Kingdom, Syria

Frequently Asked Questions

What tech stack does Arango use?

AWS, Kubernetes, Docker, Python, ArangoDB, MLflow, Triton, vLLM, TensorRT-LLM, Jenkins, CircleCI, Terraform, Ansible, and NVIDIA tooling. Also integrates with Salesforce and GCP.

What is Arango working on?

Kubernetes deployments, Python features for the unified platform, model fine-tuning and inference optimization, security monitoring and alerting, CI/CD automation, agentic workflows for autonomous agents, and AI Suite documentation.