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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** LLM, LLC - **Location:** San Francisco, CA, United States - **Experience:** Experienced - **Salary:** $180,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Automation of Tests, Cloud Computing, Continuous Delivery, Continuous Integration, Relational Databases, Design of User Interfaces, Human-Computer Interaction, Home Automation, Python (Programming Language), NoSQL, Performance Tuning, Software Engineering, TypeScript, Workflow Management Systems, Scripting, ReactJS, Large Language Models, Prompt Engineering, Reliability of Systems, Backend, Front End Software Development, Api Design, Surface Modeling - **Published:** July 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/ai-engineer-mid-level-san-francisco-ca--eb3d1ec6-6ee4-4744-a3a0-33ed50c89b8e ## About the Role Must-haves: * 2-8 years of software engineering experience with demonstrated delivery of shipped user-facing or backend products. * Practical experience deploying LLMs or LLM-based services in production, including prompt design, orchestration, and tool integration. * Proficiency across the stack: Python plus TypeScript/React (or equivalent), cloud platforms (AWS or GCP), and relational or NoSQL databases. * Working knowledge of RAG patterns, vector databases, embeddings, and retrieval pipelines, with sound judgment on when to apply each approach. * Experience building automated tests, evaluations, and monitoring for AI systems to ensure reliability beyond demos. * Experience designing API-driven, high-throughput systems and real-time product features. Nice-to-haves: * Experience with agent or workflow frameworks (e.g., LangGraph, CrewAI) and orchestration tools (e.g., Temporal, Trigger). * Familiarity with fine-tuning, parameter-efficient tuning, or multi-modal model integration. * Background building multi-tenant or enterprise-ready systems, or prior experience in regulated industries such as healthcare, fintech, or legal., Amazon Web Services (AWS), Application Programming Interface (API), Artificial Intelligence (AI), Cloud Computing, Continuous Deployment/Delivery, Continuous Integration, Customer/Client Research, Data Modeling, Engineering, Establish Priorities, GCP (Good Clinical Practices), Healthcare, High Throughput, Home Automation, Legal, Logistics, Memory Hardware, Metrics, NoSQL, Performance Modeling, Product Demonstration, Product Design, Production Systems, Python Programming/Scripting Language, React.js, Relational Databases (RDBMS), Software Engineering, Startup, Surface Modeling, Systems Maintenance, Systems Reliability, Telemetry, Test Automation, User Interface/Experience (UI/UX) ## Description Join a fast-moving, pre-seed-backed AI startup building the next generation of agentic systems that automate complex, multi-step workflows across regulated and enterprise domains - including healthcare, legal, fintech, logistics, and compliance. As a mid-level AI Engineer on the core product team, you'll own production LLM-based services end-to-end, collaborate closely with founders and product, and ship features that deliver measurable impact for real users. What You'll Do * Design, build, and maintain agentic systems that automate complex, multi-step workflows across regulated industries. * Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure, including vector databases, embeddings, and indexing for domain-specific search at scale. * Implement multi-agent orchestration, tool-calling, memory, and reasoning components to deliver robust AI-driven user experiences. * Develop evaluation and safety infrastructure to measure model performance, surface regressions, and enforce enterprise-level trust and reliability. * Ship full-stack AI products from MVP to enterprise-grade - designing APIs and data models, writing frontend and backend code, and operating production systems with CI/CD, monitoring, and testing. * Collaborate with founders, product, and design to prioritize work, define success metrics, and iterate based on user feedback and telemetry. ## Related Videos - [Watch Tests Go Brrrr! : Getting Started with Cypress in ReactJS](https://www.wearedevelopers.com/videos/282-watch-tests-go-brrrr-getting-started-with-cypress-in-reactjs) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Three years of putting LLMs into Software - Lessons learned](https://www.wearedevelopers.com/videos/1508-three-years-of-putting-llms-into-software-lessons-learned) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) - [How to Avoid LLM Pitfalls - Mete Atamel and Guillaume Laforge](https://www.wearedevelopers.com/videos/1328-how-to-avoid-llm-pitfalls-mete-atamel-and-guillaume-laforge) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)