Sr. Forward Deployed AI Solutions Engineer

Natera; Deutschland; Vor Ort; EUR 80.000 - 110.000; Vollzeit

Zusammenfassung

Natera is seeking a Senior Generative AI Engineer to design, build, and deploy advanced AI and machine learning solutions. This role focuses on creating enterprise-grade AI systems that streamline internal workflows and enhance patient outcomes.

Candidates must have deep expertise in Generative AI and be capable of implementing innovative AI solutions to automate business processes effectively.

Qualifikationen

• Deep expertise in Generative AI and machine learning engineering at scale.
• Excellent hands-on AI engineering excellence to build robust solutions.
• Understanding of AI solutions that impact patient outcomes.

Aufgaben

• Design, build, and operate LLM-powered systems in production.
• Build scalable agentic AI automation solutions.
• Implement GenAI patterns such as RAG and multi-step workflows.
• Develop and maintain data ingestion and retrieval pipelines.

Jobbeschreibung

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