Role Overview

 

The Enterprise AI Platform Engineer is responsible for build and delivery of Natera’s enterprise agentic AI platform. The enterprise AI platform will be used to prototype and build multiple agentic AI solutions using low code across Natera in a federated approach.This is a hands-on technical leadership role at the intersection of engineering excellence, low code platform design, and applied GenAI engineering. 

 

You will architect and build the core AI operating system that powers modular, low-code  enterprise AI agentic automation that is complete with agent templates, agent orchestration engine, data and MCP connectors, prompt optimization capabilities, evaluation guardrails, abstracted AI services, intelligent data extraction and reasoning capabilities. The platform empowers citizen developers, business analysts, and engineers to prototype and test AI-powered workflows using a low-code interface, while enabling developers to extend functionality through a pro-code framework.

 

Key Responsibilities

 

Platform Architecture & Core Infrastructure

• Design: Design and implement the core architecture of the Enterprise AI Platform — low code, modular, scalable, and secure.

• Agentic Orchestration: Build the agent orchestration runtime, including task queues, state management, and inter-agent communication.

• Complexity: Architect for long-running, resilient AI workflows, enabling agents to execute and monitor multi-step, asynchronous processes.

• Low code abstraction: Develop APIs and services for automation, evaluation, and agent lifecycle management.

• Deployment: Establish DevOps, CI/CD pipelines, and configuration management to ensure smooth deployment at scale.

 

Low-Code/Pro-Code Experience

• Low-Code Interface: Build an intuitive visual builder that allows business users to compose agent workflows through drag-and-drop and configuration.

• Pro-Code Mode: Provide a developer extension layer where engineers can author and deploy agents in code (Python, TypeScript) directly into the same framework.

• Unified Runtime: Ensure both low-code and pro-code workflows share common infrastructure for orchestration, evaluation, and governance.

• Transparency & Debugging: Surface workflow traces, model evaluations, and output explanations directly in the user interface.

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