AI Engineer

Above the Treeline•Ann Arbor, MI

11h•$130,000 - $165,000•Remote

About The Position

We’re looking for a Senior AI Engineer who’s excited to build real, production-ready features that make a meaningful impact on the book industry. In this role, you’ll work across our stack (C#, JavaScript/TypeScript, React/Node) while also integrating modern AI capabilities — including LLMs, RAG pipelines, vector search, and AI-driven analytics. This isn’t a research role. It’s a hands-on engineering position where you’ll ship features, improve our search and analytics systems, help build an LLM-powered chatbot, and use AI coding tools (primarily Claude Code) to accelerate development. You’ll also help share and model AI best practices with the team as we adopt new tools and approaches. If you love turning ideas into usable, reliable software — and you enjoy pairing your own engineering skills with AI tooling — you’ll be right at home here. This is a senior individual contributor role with a strong hands-on focus, offering influence through mentorship and technical leadership rather than formal people management.

Requirements

Solid professional experience in software engineering (5+ years ideal, but flexible for the right person).
Strong coding ability in:
C# / .NET
JavaScript / TypeScript
React and Node
Experience integrating LLMs or AI APIs or clear, demonstrated ability to ramp up quickly.
Understanding of modern AI application patterns:
Prompt engineering
RAG concepts (embeddings, chunking, vector search)
Model limitations, hallucination behaviors, and evaluation approaches
Familiarity with cloud services (Azure preferred).
Comfortable using AI code generation tools daily (Claude Code especially).
Strong problem-solving, communication, and collaboration skills.
Ability to deliver high-quality work independently while also being a supportive team player.

Nice To Haves

Python (useful for experimentation, data tasks, and AI tooling)
Experience with:
Elasticsearch/SOLR or other search engines
Vector databases (Redis, Azure Search, Pinecone, pgvector, etc.)
Analytics pipelines or data modeling
Training or fine-tuning small models
Understanding of EDI workflows and systems
Background in publishing or book-related data (not required)

Responsibilities

Use AI Tools as an Engineering Multiplier
Make regular use of Claude Code and similar tools for code generation, debugging, documentation, and refactoring.
Model healthy, thoughtful AI-assisted development practices — when to trust, when to verify, and how to iterate.
Share prompts, patterns, and best practices with the team to improve collective output.
Own AI-enabled solutions end to end — from design and implementation through production behavior, monitoring, and iteration.
Evaluate AI tools, models, and approaches for fit, tradeoffs, and risk within the scope of the product.
Retain responsibility for validating AI-generated outputs and deciding what code, designs, and behaviors are ultimately shipped.
General Software Engineering
Write, test, and maintain high-quality code using C#, JavaScript/TypeScript, Node, React, and SQL.
Contribute to APIs, services, and full-stack features that interact with AI components.
Participate in code reviews with an eye toward clarity, maintainability, security, and performance.
Troubleshoot complex issues related to search, data pipelines, model performance, latency, and scaling.
Build AI-Powered Product Features
Design and deliver AI-enabled capabilities across the Edelweiss platform, including improved search, analytics, and user workflows.
Build and refine our LLM-driven chatbot using RAG pipelines, embeddings, and vector search.
Integrate AI and ML services into existing products in ways that are safe, reliable, and customer-centered.
Help design and evolve architectures for prompt handling, retrieval, ranking, caching, and model-related performance tuning within the systems you work on.
Collaboration, Leadership, and Team Development
Mentor teammates on AI tooling, prompting, RAG architecture, and safe, ethical AI usage through pairing, reviews, and knowledge sharing.
Communicate status, risks, and tradeoffs clearly to technical and non-technical partners.
Work cross-functionally with product, QA, and other dev teams to ensure AI features deliver actual value.
Contribute to evolving our internal AI standards and longer-term direction, in collaboration with the broader engineering team.
Clearly communicate AI tradeoffs, limitations, and risks to teammates and non-technical partners.