Role Overview

The Senior Generative AI Engineer is responsible for designing, building, and deploying Natera’s Generative AI and Machine Learning solutions. The role needs excellent hands-on AI engineering excellence to build robust, accurate, and reliable AI solutions. This role requires deep expertise in Generative AI and machine learning engineering at scale, with a passion for building solutions that directly impact patient outcomes and clinical innovation.

 

In this role, you will develop enterprise-grade AI/ML solutions that power internal workflows (R&D, Lab Ops, Clinical Trials, Billing, Patient/Provider engagement) to automate business processes.

You will  also build cutting-edge AI solutions leveraging agentic architecture, retrieval-augmented generation (RAG), vector search, feature stores, LLMOps, experimentation, observability, and compliance-first AI pipelines.

 

Key Responsibilities

• Design, build, and operate LLM-powered systems used in production, from initial design through deployment and iterations

• Build scalable agentic AI automation solutions, selecting appropriate patterns (reasoning, memory, agent frameworks, MCP’s, workflow orchestration, fine-tuning) based on business requirements

• Implement GenAI patterns such as RAG, tool/function calling, and multi-step workflows, selecting approaches based on accuracy, reliability and cost

• Develop and maintain data ingestion and retrieval pipelines, especially for unstructured or semi-structured documents

• Fine-tune and adapt open-source or commercial LLMs for domain-specific tasks when appropriate

• Set quality, evaluation, and reliability standards for GenAI systems, including testing, monitoring, observability, and failure handling

• Make system-level tradeoffs across model choice, latency, cost, accuracy, and operational complexity, and guide teams through those decisions

• Deploy and monitor GenAI services on AWS, optimizing for latency, cost, and system stability

• Collaborate with product managers and domain experts to translate requirements into technical solutions

• Establish golden paths (templates, examples, docs) and contribute to shared GenAI libraries, patterns, and best practices used by other engineers

• Provide technical guidance and mentorship to mid-level engineers