Natera is looking to build and deliver an enterprise agentic AI platform to enable the prototyping and building of multiple agentic AI solutions using low code across the company in a federated approach. This platform will serve as the core AI operating system for modular, low-code enterprise AI agentic automation.
Requirements
- Proven expertise in implementing workflow orchestration or automation systems
- Proficiency in Python with deep experience in backend architecture and API design.
- Experience with low-code/no-code automation platforms (Zapier, n8n etc.), internal developer platforms (IDPs), or workflow engines (Temporal, Airflow).
- Experience in working with well known agentic cloud platforms (e.g. AWS Bedrock agents, AgentCore etc.).
- Hands-on experience with LLMs, RAG, vector databases, and orchestration frameworks (LangChain, LlamaIndex, AutoGen, DSPy).
- Fluency in cloud infrastructure, Kubernetes, Docker, and CI/CD automation.
- Knowledge of observability and telemetry systems for event-driven environments.
Responsibilities
- Design and implement the core architecture of the Enterprise AI Platform — low code, modular, scalable, and secure.
- Build the agent orchestration runtime, including task queues, state management, and inter-agent communication.
- Develop APIs and services for automation, evaluation, and agent lifecycle management.
- Establish DevOps, CI/CD pipelines, and configuration management to ensure smooth deployment at scale.
- Build an intuitive visual builder that allows business users to compose agent workflows through drag-and-drop and configuration.
- Provide a developer extension layer where engineers can author and deploy agents in code (Python, TypeScript) directly into the same framework.
- Build services to retrieve information from unstructured data using vector databases and retrieval pipelines.
Other
- 12+ years of software engineering experience, with 8+ years in platform or distributed systems architecture.
- Technical Leadership & Collaboration
- Collaborate with business stakeholders, data scientists, and product teams to identify automation use cases and measure ROI via evaluation metrics.
- Mentor future engineers and contribute to an engineering culture centered on safety, transparency, and impact.
- Continuously explore emerging agent frameworks, vector stores, and evaluation methodologies.
