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# AI Engineer

- **Company:** Recruiting from Scratch
- **Location:** San Francisco, CA, United States
- **Experience:** Starter
- **Salary:** $200,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, User Authentication, Programming Tools, Interoperability, Python (Programming Language), PostgreSQL, Redis, OpenAI, Session Management, Openapi, AI Infrastructure, Large Language Models, Backend, Agentic-AI, Fastapi, Free and Open-Source Software, Api Design
- **Published:** November 7, 2026
- **Apply:** https://www.careerbuilder.com/job-details/ai-engineer-san-francisco-ca--0350b7b6-b4c0-4ba6-80e5-799cf3b80bc6

## About the Role

Artificial Intelligence (AI), Needs Assessment, Team Lead/Manager, Authentication, Budgeting, Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, MCP - Microsoft Certified Professional, Interoperability, Application Programming Interface (API), * 1-2+ years of professional backend or full-stack engineering experience building production systems.

 * Strong Python experience.
 * Experience building APIs and backend services, ideally using technologies such as FastAPI.
 * Meaningful hands-on experience working with the OpenAI API.
 * Understands the differences between /chat/completions and /responses and can discuss API-specific behavior and implementation nuances.
 * Strong understanding of modern LLM APIs and how developers integrate models into production applications.
 * Comfortable learning the APIs and behavior of different model providers and translating between them.
 * Strong engineering fundamentals around API design, reliability, and production software.
 * Comfortable operating with significant ownership on a small engineering team.
 * Interested in speaking directly with users and solving real developer problems.
 * High level of curiosity and interest in the rapidly evolving AI infrastructure ecosystem.

Preferred

 * Experience building LLM infrastructure, AI gateways, or developer tooling.
 * Experience working across multiple LLM providers rather than exclusively with a single model API.
 * Familiarity with Anthropic's API in addition to OpenAI.
 * Experience with MCP and emerging agent infrastructure.
 * Experience building or contributing to open-source software.
 * Experience maintaining SDKs, APIs, or developer-facing infrastructure with significant external usage.
 * Familiarity with Redis and PostgreSQL.
 * Experience thinking deeply about developer experience and API ergonomics.
 * Comfortable working directly with founders in a fast-moving early-stage environment.

## Description

Recruiting from Scratch is a specialized talent firm dedicated to helping companies build exceptional teams. We partner closely with our clients to deeply understand their needs, then connect them with top-tier candidates who are not only highly skilled but also the right fit for the company's culture and vision. Our mission is simple: place the best people in the right roles to drive long-term success for both clients and candidates.
https://www.recruitingfromscratch.com, * Build and maintain the interoperability layer that provides a unified interface across a large number of LLM APIs and providers.

 * Develop transformations between OpenAI and Anthropic API specifications and the native formats of other model providers.
 * Build provider-agnostic functionality across LLM APIs, including session management and /v1/responses functionality.
 * Design gateway-level systems supporting provider capabilities such as skills, compaction, batches, and other emerging model features.
 * Build developer infrastructure used by millions of users through the company's Python SDK.
 * Work across LLMs, MCP, and AI agents, helping create a consistent developer experience across rapidly evolving ecosystems.
 * Support a wide variety of MCP authentication flows and agent use cases.
 * Work directly with the CEO and CTO on architecture, product, and engineering decisions.
 * Engage directly with developers and customers to understand problems and translate feedback into product improvements.
 * Ship quickly and take meaningful ownership over both implementation and product decisions.