Senior AI Engineer

Wipfli Advisory LLC

12d•Remote

About The Position

Wipfli is building the next generation of AI‑powered solutions that transform how our professionals deliver value to clients. As a Senior RAG Engineer, you will play a pivotal role in advancing our AI‑driven functional requirements documentation platform, an enterprise application used across the firm to automate the creation of complex project documentation. This role blends software engineering, machine learning, and enterprise application development. You’ll work closely with data scientists, engineers, and business teams to build production‑grade retrieval and generation systems that make a measurable impact on hundreds of users. If you’re passionate about building real AI products, not demos or POCs, and you want to shape the foundation of a growing AI capability, this is your opportunity.

Requirements

Bachelor's degree in Computer Science, Information Management, Data Science, or AI related field
3-5 years of experience in data science and AI roles
7-10 years of experience software development roles
Demonstrated track record of building production-grade AI applications from conception to deployment
Demonstrated deep understanding of RAG methodologies and practical application.
Experience with document processing and OCR technologies, including integration and deployment within enterprise environments

Nice To Haves

Master's degree or advanced certification in Data Science or AI
4+ years of full stack development experience with modern frameworks (React, Vue, or Angular; Node.js, Python, or similar; .NET experience
Experience with frameworks such as LangChain, LlamaIndex, or Haystack

Responsibilities

Design, develop, and optimize full‑stack applications using RAG for high‑accuracy retrieval and generation
Build and maintain APIs and microservices with monitoring and logging for reliability and performance
Implement vector databases and semantic search; fine‑tune retrieval for strong precision and recall
Optimize chunking, embeddings, and retrieval parameters tailored to professional services content
Evaluate LLM architectures and maintain prompt‑engineering frameworks to improve response quality
Build ETL pipelines and integrate with firm platforms such as document management, CRM, and engagement systems
Ensure data quality, security, and compliance, including role‑based access, encryption, audit logging, and confidentiality
Partner across teams—engagement, data science, and ML—to deliver capabilities and mentor junior engineers
Stay current on emerging AI/ML technologies and assess applicability to enterprise use cases