> Markdown version of [/jobs/ext/569403-jr-ai-engineer](https://www.wearedevelopers.com/jobs/ext/569403-jr-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # JR. AI ENGINEER - **Company:** Revi Inc - **Location:** San Francisco, CA, United States - **Experience:** Starter - **Salary:** $130,000.0 - $170,000.0 - **Contract:** Internship / Graduate position - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Distributed Computing Environment, Python (Programming Language), Machine Learning, Standard Sql, Azure Machine Learning, SQL Databases, Speech Recognition, Core Voice Platform, Pytorch, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Apache Spark, Git, Scikit Learn, Information Technology, Production Code, Xgboost, Apache Kafka, Machine Learning Operations, Restful APIs, Stream Processing, GPT, Databricks - **Published:** June 19, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=ea0a61f7d307ef4b ## About the Role Do you have experience in SQL databases?, Do you have a Bachelor's degree?, 0-3 years of experience building AI/ML systems. This can come from full-time work, internships, or substantive class/research projects - what matters is depth, not the source Demonstrable hands-on experience with agentic AI - single-agent or multi-agent systems built using frameworks like LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, or custom orchestration Working knowledge of deterministic ML modeling - building, training, and evaluating models with libraries like scikit-learn, XGBoost, LightGBM, or PyTorch Hands-on experience integrating LLM APIs (Claude, GPT, Gemini, etc.) into a working application - including prompt design, tool calling, and handling streaming/structured responses Some deployment experience - you've put a service behind an API, containerized it, and run it somewhere real (AWS/GCP/Azure, Modal, Render, Fly, etc.) Strong Python skills; comfort with Git, REST APIs, and basic SQL BS or MS in Computer Science, Machine Learning, or a related quantitative field - or equivalent demonstrable skill Preferred Qualifications Experience with vector databases (Pinecone, Weaviate, pgvector, etc.) and RAG pipelines Familiarity with LLM observability/eval tools (LangSmith, Langfuse, Braintrust) Exposure to distributed data processing (Spark, Databricks) or streaming systems (Kafka, Kinesis, Pub/Sub) - even from an internship or coursework Exposure to voice AI, speech-to-text, or real-time conversational systems Experience with restaurant tech, commerce platforms, or B2B SaaS ## Description We're hiring a Junior AI Engineer to help us design, build, and ship agentic AI systems and ML-powered features into production. You'll work alongside senior engineers on real problems - building agents that take actions on behalf of restaurants, tuning models that personalize customer experiences, and integrating frontier LLMs into our product stack. This is a hands-on builder role; expect to write production code, deploy services, and iterate quickly based on user feedback. What You'll Do Build and ship agentic AI features - single-agent and multi-agent systems that perform real tasks for restaurants and their customers Develop deterministic ML models (classification, ranking, regression, retrieval) for use cases across the platform Integrate LLM APIs (Anthropic Claude, OpenAI GPT, and others) into product workflows, including prompt engineering, tool use, structured outputs, and retrieval-augmented generation Deploy and maintain AI/ML services in production - containerized, observable, and reliable Work closely with senior engineers and product to translate fuzzy product ideas into shipped features Contribute to evaluation and monitoring frameworks so we know our agents and models actually work ## Related Videos - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation](https://www.wearedevelopers.com/videos/1235-carl-lapierre-exploring-advanced-patterns-in-retrieval-augmented-generation) - [Explainable machine learning explained](https://www.wearedevelopers.com/videos/589-explainable-machine-learning-explained) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## 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) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)