Senior Platform AI Engineer

WeHireYou; India; Hybrid; INR 2,500,000 - 4,800,000; Full time

Job summary

Drata’s AI Platform team builds the production infrastructure that powers AI features across our compliance platform. You’ll own systems between AI models and customers, including tool definitions, deployment pipelines, and orchestration layers for multi-step workflows with persistent state.

You’ll work with agent developers, product engineers, and an embedded SRE partner to minimize time-to-launch for new agents and maximize output quality across 15 workflows.

Responsibilities

• Design and build MCP servers exposing platform tools for AI agents, with clear tool schemas and context management.
• Create and operate multi-step agent workflows with state, routing, and long-running processes.
• Own LLM workflow operations: model upgrades, prompt versioning, A/B testing, and deployment compatibility.
• Analyze token budgets, costs, and context limits; troubleshoot prompt/model/infrastructure issues.

Job description

Senior Platform AI Engineer

Our Mission & Values:

At Drata, we help companies earn and keep the trust of their users, customers, partners, and prospects. We’re the proof layer that shows great companies deserve the trust they aim to build.

We live our values every day. Built on Trust means consistency is everything. Act with Integrity by always doing the right thing. Being Customer-Obsessed keeps the people we serve at the center of our work. Competitive Fire drives us to push ourselves harder than anyone else. Diversity brings unique perspectives that lead to better solutions. Automation First ensures we save time and money by making efficiency a priority.

Our Culture & Work Style

At Drata, we’re not just building software - we’re building a mindset. Everything we do springs from:

• Be a Driver (Owner-Operator Mentality): Own your work. Improve relentlessly. Deliver results.


• Move at Drata Speed (Precision & Velocity): Fast decisions. Quick learning. Immediate impact.


• Stay Mission-Driven (Customer-Obsessed): Challenge assumptions. Deliver value. Stay hungry.

We pair that high-velocity culture with a thoughtful hybrid model because we believe flexibility and collaboration both matter. That’s why in the Bay we come together in-office Tuesday through Thursday our high-impact collaboration days where teams align, strategize, and innovate. Mondays and Fridays are flexible, giving you space for focused work, balance, and autonomy.

If you thrive when you’re empowered, energized, and working with smart, mission-driven people, you’ll feel at home here.

Why Join The Drata Team?

The best way to understand the Driver’s Mindset is to see it in action. We’re an award-winning, mission-driven team of 600+ people worldwide, united by a culture that values trust, speed, and continuous growth.

• See the Speed: Watch our CEO, Adam Markowitz, discuss the hyper-growth journey, from $0 to $100M ARR in just four years


• Hear the Voice of the Team: Explore our \"Life at Drata\" page for employee testimonials on our collaborative and the growth opportunities available.


• Experience the Impact: See why we are consistently recognized on Fortune's Best Workplaces lists.


• Connect with Us on Socials: LinkedIn - follow us for company updates, employee stories, and career news.

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...