Senior Generative AI Engineer

Natera is a global leader in cell-free DNA testing, dedicated to oncology, women’s health, and organ health. The Senior Generative AI Engineer will design, build, and deploy Generative AI and Machine Learning solutions to enhance patient outcomes and clinical innovation.

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

Requirements:

• 8+ years of experience in software engineering, ML engineering, or applied AI
• Proven experience building and operating LLM-based systems in production
• Strong Python skills and experience with PyTorch and/or Hugging Face
• Experience building agentic AI solutions using agent frameworks (LangChain, CrewAI etc.) and agent execution engines (AWS Bedrock etc.)
• Solid understanding of RAG architectures, embeddings, vector databases, and prompt orchestration
• Experience deploying AI systems on AWS (e.g., SageMaker, Bedrock, EKS/ECS, Lambda, S3)
• Strong debugging skills and comfort working across model, data, and infrastructure layers
• Ability to work independently on complex problems and communicate clearly with cross-functional partners
• Masters degree or PhD in Computer Science, AI/ML, engineering or related field
• Experience in healthcare, pharma, diagnostics, or other regulated industries
• Familiarity with AI governance frameworks, bias detection, explainability, and compliance (e.g., HIPAA, CLIA, FDA)