Senior AI Engineer, Legal - USA Remote

Danaher is hiring a Senior AI Engineer to design, build, and deploy AI-powered solutions for legal and compliance workflows across its corporate, platform, and operating-company functions. The role combines software engineering, data science, and legal operations to deliver secure, production-grade AI applications and support adoption by legal teams.

Responsibilities:

• Partner with legal stakeholders to translate workflows into technical solutions — Conduct discovery with attorneys and compliance professionals across Danaher to understand their workflows, identify automation opportunities, and deliver solutions that legal teams trust and adopt
• Architect and build AI-powered legal applications. Design end-to-end solutions for contract review, legal research, regulatory analysis, and compliance workflows using LLMs, retrieval-augmented generation (RAG), and agentic AI frameworks. Develop and optimize document processing pipelines. Build scalable ingestion, parsing, chunking, embedding, and indexing systems for legal content (contracts, playbooks, regulatory filings) to power intelligent search and analysis
• Ship production-grade AI features. Write clean, well-tested code (Python and/or TypeScript) to deliver production applications using LLM APIs, orchestration frameworks (e.g., LangChain, LlamaIndex), and CI/CD pipelines — from prototype through deployment and monitoring. Design evaluation and quality framework.  Build automated evaluation pipelines, test sets, and quality metrics specific to legal AI outputs, ensuring accuracy, consistency, and defensibility of AI-generated work product
• Embed governance and security by design — Implement data security controls, access management, audit logging, PII redaction, and data residency compliance appropriate for applications handling privileged and confidential legal content. Apply product thinking to legal AI solutions — Understand user needs through discovery and feedback, translate them into clear product and technical requirements, and help prioritize features that drive adoption and measurable business value
• Technical leadership and mentorship  - Lead, educate and mentor legal team members in developing no-code AI solutions. Serve as the technical bridge between legal and engineering. Communicate clearly across disciplines, deliver demos and training to legal users, and channel user insights back into product development and roadmap priorities. Partner closely with IT, Data & AI teams to design, build, and deliver scalable AI solutions aligned with enterprise architecture and governance standards

Requirements:

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related technical field
• 5+ years of professional experience in software engineering, data science, or machine learning engineering, with a track record of shipping production applications
• Production experience with large language models (LLMs), including prompt engineering, fine-tuning, RAG architecture, agent/tool-use development, and evaluation frameworks
• Strong programming proficiency in Python, with experience in AI/ML libraries and frameworks (e.g., LangChain, LlamaIndex, Hugging Face, or equivalent orchestration tools)
• Demonstrated proficiency leveraging AI development tools (e.g., Claude Code, Cursor, GitHub Copilot) to accelerate software solution development in production environments
• Hands-on experience with modern AI infrastructure, including vector databases (e.g., Pinecone, Weaviate, Qdrant), cloud platforms (Azure, AWS, or GCP), API design, and containerized deployment (Docker, Kubernetes)
• Experience designing data pipelines and document processing systems for unstructured content at scale (parsing, OCR, entity extraction, classification)
• Demonstrated interest or experience in legal, compliance, or regulatory domains — e.g., prior work in legal tech, contract analytics, regulatory technology, legal operations, paralegal experience, law school coursework, or JD
• Master's or advanced degree
• proficiency in an additional language (TypeScript, Java)
• Building or deploying AI solutions in regulated industries (life sciences, healthcare, financial services, legal)
• Agentic AI patterns — multi-step autonomous workflows with human-in-the-loop oversight, tool calling, MCP servers, and structured outputs
• Enterprise AI governance and responsible AI practices, including model evaluation, bias testing, explainability, and compliance with frameworks such as the EU AI Act