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# Tech Stack Frontend

- **Company:** Shepherd AI LLC
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Cloud Computing, Distributed Systems, Machine Learning, AI Infrastructure, Large Language Models, Prompt Engineering, Model Validation, Graphql
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/software-engineer-applied-ai-shepherd-8135379

## About the Role

* Strong backend engineering experience building scalable APIs and distributed systems
 * Hands-on experience shipping AI-powered products into production
 * Familiarity with LLMs, RAG architectures, embeddings, and vector databases
 * Experience integrating AI systems into user-facing or workflow-driven applications
 * Comfort with cloud infrastructure and deploying AI workloads at scale
 * A product mindset and the ability to translate ambiguous business problems into concrete AI systems
 * You have at least 5+ years of working experience (preferably as a full-stack engineer) with a track record of delivering high-impact products

Bonus Points

 * Experience building AI agents or multi-step reasoning systems
 * Background in applied machine learning beyond generative AI
 * Experience with pricing systems, financial infrastructure, or risk modeling
 * Interest in transforming legacy industries with modern AI systems

## Description

We are hiring a Software Engineer focused on Applied AI to build intelligent systems that directly power our underwriting and pricing engine. This role sits at the intersection of product engineering, AI infrastructure, and real-world insurance workflows.

You will design and ship production AI systems that meaningfully improve how we evaluate risk, price policies, and operate our business.

What You'll Do

 * Partner with product, engineering, underwriting, and data teams to identify high-leverage AI opportunities
 * Design, build, and deploy LLM-powered features across underwriting, pricing, and internal tooling
 * Develop AI agents and retrieval-augmented systems that extract structured insights from unstructured insurance data
 * Build production-grade AI infrastructure for model serving, inference, evaluation, and monitoring
 * Create internal AI tools that enable underwriters and operators to move faster with higher quality decisions
 * Establish best practices for prompt engineering, evaluation frameworks, and model reliability
 * Own AI features end-to-end from ideation to production, * React & Next.js
 * Typescript
 * GraphQL w/ Apollo

Backend

 * Node.js
 * Postgres & Redis
 * Typescript
 * GraphQL APIs


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