You will build the AI the company runs on internally. You will build the layer that enables, guides, and controls AI efforts across the organization- the identity, access, and audit backbone for internal AI — along with the assistants and automations that run safely on top of it. This is a hands-on infrastructure role with a security, fin-ops, and DevOps reflex built in. You will help teams move quickly as you will define the infra they should use and we manage this infra.
What you'll own
Platform ownership. Own the full lifecycle of internal AI and automation platforms — runtime infrastructure, CI/CD, and identity controls, end to end.
Identity, cost & control. Implement identity, boundaries, technology, cost, efficiency.
Workflow architecture. Design and service automations and custom AI assistants for internal teams (Support, Marketing, Legal, Finance, HR, and beyond)to help solve real operational pain.
Research & tech scouting. Continuously evaluate emerging models, agent frameworks, and SaaS tools and onboard and implement them in Artlist.
Standardization. Establish reusable design patterns, integrations, and prompt libraries so teams can automate their own work safely, efficiently and smoothly, for the best experience..
Requirements
3+ years in DevOps, SRE, or Production Engineering, with a genuine operational and reliability mindset.
Coding and scripting skills automation, API integrations, and internal tooling.
Solid DevOps foundation: Infrastructure as Code (Terraform), containerization (Docker), CI/CD pipelines, and secrets management.
Identity & API fundamentals. Practical, hands-on knowledge of OAuth2, SSO/SCIM, REST APIs, webhooks, and scoped service accounts.
Practical LLM experience. Hands-on work with LLMs (Claude, ChatGPT APIs, prompt engineering, writing skills, MCPs and Plugins).
Cross-functional communication. You can sit with a non-technical stakeholder, understand their actual pain, and turn it into a clean, safe automation.
Cloud Infrastructure : Hands-on experience managing and architecting cloud infrastructure, with specific proficiency in AWS.
Nice to have
Experience with workflow and automation platforms (n8n, Temporal, Workato, make, Hermes).
Exposure to AI gateway or routing layers (LiteLLM, OpenRouter, Bedrock).
Familiarity with vector databases, RAG concepts, or LLM observability tools (Langfuse, Helicone).
A background in IT infrastructure or corporate systems automation.
