Senior Platform AI Engineer

Drata; San Francisco (CA); On-site; USD 192,000 - 259,800; Full time

Benefits offered by this job

Stock equity

Up to 100% employer-paid health insurance

401(k) plan

Paid parental leave

Flexible vacation policy

Job summary

Drata's AI Platform team seeks a Software Engineer to develop critical AI infrastructure. You'll design MCP servers that connect our compliance platform to AI agents, build workflows, and manage the lifecycle of LLM models. This role requires at least 7 years of software engineering experience and proficiency in Python, with a strong emphasis on cloud infrastructure and AI tooling.

Join us to make significant impacts while enjoying competitive salary, comprehensive health benefits, and professional development opportunities.

Qualifications

• 7+ years of software engineering experience.
• 2+ years of building or operating AI/ML infrastructure.
• Experience with cloud infrastructure, preferably AWS.
• Understanding of AI-specific tooling like LLM APIs.
• Excellent communication skills regarding technical decisions.

Responsibilities

• Design and build MCP servers for AI platforms.
• Develop infrastructure for multi-step agent workflows.
• Own operational aspects of LLM workflows.
• Manage production AI infrastructure and RAG systems.
• Enable product teams to ship AI features faster.

Skills

Software engineering experience

AI/ML infrastructure

Python

TypeScript/Node.js

LLM APIs

Communication of technical tradeoffs

Tools

Terraform

AWS

vLLM

Job description

Job Summary

Drata's AI Platform team builds the production infrastructure that powers AI features across our compliance platform — from MCP servers that make Drata's data available to AI agents, to LLM workflow orchestration that automates SOC 2, TPRM, and policy analysis. You'll own the systems that sit between our AI models and our customers: tool definitions that agents actually understand, deployment pipelines that handle model upgrades without breaking output quality, and orchestration layers that manage multi‑step agent workflows with persistent state.

This is not a traditional infrastructure role. You'll debug prompt templates alongside Terraform modules. You'll design API schemas optimized for LLM token budgets, not just HTTP throughput. When a model upgrade changes behavior across 15 workflows, you'll assess quality impact — not just confirm the containers are healthy. You'll work closely with our agent developers, product engineers, and an embedded SRE partner, sitting at the intersection of AI development and production reliability. Our north star is simple: minimize the time it takes to launch a new agent in production. You're someone who asks "are we solving the right problem?" before writing the first line of code, who builds systems that make five other engineers faster, not just yourself, and who's equally proud of what they chose not to build.

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. This means making architectural decisions about tool granularity, naming conventions for agent disambiguation, response compression for LLM context windows, and workspace isolation for multi‑tenant access. You'll own the protocol layer that determines whether agents can reliably find and use the right tools — writing semantic parameter descriptions, contextual hints, and tool schemas that optimize for model comprehension, not just developer ergonomics.

• 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. Design systems that handle agent failure modes gracefully: retries on ambiguous tool outputs, fallback strategies when models produce unexpected results, and observability into multi‑step execution traces.

• LLM Operations & Model Lifecycle Management
  • Own the operational side of our LLM workflows: model upgrades across production pipelines (assessing behavior changes, not just version bumps), prompt versioning and A/B testing, AI workflow deployment with custom container compatibility, and output quality monitoring.
  • Manage token capacity planning — understanding model costs, context limits, batching strategies, and rate governance across workflows. When an AI workflow fails, you'll investigate whether it's a prompt template issue, a model behavior change, or an infrastructure problem. Making that distinction requires understanding both systems.
• Production AI Infrastructure & RAG Systems

  Operate and evolve our production AI stack: vector storage and indexing (designing chunking strategies and metadata schemas for retrieval quality), document parsing pipelines, multi‑region deployment, and cost optimization across LLM providers. You'll make RAG architecture decisions — embedding strategies, retrieval filtering, data model coordination — where the engineering challenge is search quality, not just system uptime. Implement caching layers and token‑aware request routing to manage spend as AI workloads scale.

• 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 alongside traditional infrastructure monitoring. Enable product engineering teams to ship AI features faster by providing reliable, well‑documented platform primitives.

What You'll Bring

• 7+ years of software engineering experience, with 2+ years building or operating AI/ML infrastructure in production.
• Strong in Python; TypeScript/Node.js is a nice‑to‑have.
• Experience with LLM APIs, vector databases, or AI orchestration platforms and understanding the difference between "the service is up" and "the model output is good." Comfortable across the stack: Terraform, prompt debugging, agent orchestration framework.
• Cloud infrastructure experience (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, etc), vector databases, embedding pipelines, prompt management platforms, or agent frameworks.
• Excellent communication of technical tradeoffs, especially when explaining AI‑specific infrastructure decisions to stakeholders. Own what you see broken and spot architectural decisions that will fail at scale early, clearly, and with an alternative.

How We Support You

• Shared Success: We provide stock equity to ensure you share directly in our growth.
• Health & Wellness: Up to 100% employer‑paid premiums for medical, dental, and vision coverage for employees and dependents, plus comprehensive wellness benefits and healthcare concierge services.
• Financial Well‑being: 401(k) plan, company‑paid life and disability insurance, tax‑advantaged spending accounts, and a range of discounted voluntary offerings.
• Family Support: Paid parental leave policy after six months, fertility and family‑building benefits, and dedicated leave specialists.
• Growth & Development: Annual stipends for professional and personal development, internal learning opportunities.
• Time Off & Flexibility: Flexible vacation policy, paid holidays, and other perks for rest and recovery.

This role will receive a competitive base salary, benefits, and stock, typically in the form of Restricted Stock Units (RSUs). The applicable salary range is $192,000 - $259,800. A variety of factors are considered when determining someone’s leveling and compensation – including a candidate’s professional background and experience. These ranges may be modified in the future and final offer amounts may vary.