Senior Platform AI Engineer

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
• Write semantic parameter descriptions, contextual hints, and tool schemas optimized for model comprehension

Agent Orchestration & Workflow Infrastructure

• Build and operate the infrastructure for deploying multi-step agent workflows
• Manage state management across complex reasoning chains, tool routing and execution runtimes, and long-running agentic processes
• Design systems that handle agent failure modes gracefully: retries on ambiguous tool outputs, fallback strategies, and observability into multi-step execution traces

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
• Manage token capacity planning, model costs, context limits, batching strategies, and rate governance
• Investigate AI workflow failures to distinguish between prompt template issues, model behavior changes, or infrastructure problems

Production AI Infrastructure & RAG Systems

• Operate and evolve production AI stack: vector storage and indexing, document parsing pipelines, multi-region deployment, and cost optimization
• Make RAG architecture decisions around embedding strategies, retrieval filtering, and data model coordination
• Implement caching layers and token-aware request routing to manage spend

Platform Enablement & Developer Experience

• Build CI/CD patterns specific to AI workflows (reproducible deployments, SDK version compatibility, workflow rollback semantics)
• Own AI-specific observability — token usage dashboards, response quality metrics, agent execution traces, and cost-per-workflow tracking
• Enable product engineering teams to ship AI features faster

What you'll bring

• 7+ years of software engineering experience, with 2+ years building or operating AI/ML infrastructure in production
• Strong in Python (AI services built in Python); TypeScript/Node.js a plus
• Experience with LLM APIs, vector databases, or AI orchestration platforms
• Experience across the stack: writing Terraform, debugging prompt templates, designing agent orchestration frameworks
• Experience in cloud infrastructure (AWS preferred — ECS, S3, Bedrock), container orchestration, infrastructure-as-code, CI/CD pipeline design, API design, workflow orchestration engines, and distributed systems
• Experience with AI-specific tooling: LLM APIs (Claude, OpenAI, etc), model serving frameworks (vLLM, SageMaker), vector databases, embedding pipelines, prompt management platforms, or agent frameworks
• Clear communication about technical tradeoffs, especially AI-specific infrastructure decisions