Senior Platform AI Engineer

Drata is the proof layer that helps companies earn and keep the trust of their users, customers, partners, and prospects. As a Senior Platform AI Engineer you'll own the systems that sit between Drata's AI models and its customers: tool definitions that agents understand, deployment pipelines that handle model upgrades without breaking output quality, and orchestration layers that manage multi-step agent workflows with persistent state. Drata's AI Platform team builds production infrastructure for AI features including MCP servers and LLM workflow orchestration.

What you'll do:

• MCP Server Development & AI-Optimized API Design: Design and build MCP (Model Context Protocol) servers that expose Drata's platform to AI agents. Make architectural decisions about tool granularity, naming conventions for agent disambiguation, response compression for LLM context windows, and workspace isolation for multi-tenant access. Own the protocol layer that determines whether agents can reliably find and use the right tools.
• Agent Orchestration & Workflow Infrastructure: Build and operate the infrastructure for deploying multi-step agent workflows: state management across complex reasoning chains, tool routing and execution runtimes, and long-running agentic processes that persist over time. Own the orchestration layer that coordinates agent planning, tool calls, and human-in-the-loop patterns.
• LLM Operations & Model Lifecycle Management: Own the operational side of LLM workflows: model upgrades across production pipelines, prompt versioning and A/B testing, AI workflow deployment, and output quality monitoring. Manage token capacity planning.
• Production AI Infrastructure & RAG Systems: Operate vector storage, document parsing pipelines, RAG systems, and cost optimization across LLM providers.
• Platform Enablement: Design AI-specific CI/CD patterns and observability dashboards for workflow quality tracking.

What you'll bring:

• 7+ years software engineering; 2+ years building/operating AI/ML infrastructure in production
• Python expertise (primary); TypeScript/Node.js (secondary)
• Cloud infrastructure (AWS: ECS, S3, Bedrock), container orchestration, infrastructure-as-code (Terraform)
• Experience with LLM APIs (Claude, OpenAI), model serving frameworks (vLLM, SageMaker), vector databases, embedding pipelines, prompt management platforms, and agent frameworks

Hybrid from the San Francisco office (in-office Tuesday-Thursday). Base salary range $192,000 - $259,800.