<h3>About Us</h3><p>Constructor is the next-generation platform for search and discovery in ecommerce, built to explicitly optimize for metrics like revenue, conversion rate, and profit. Our search engine is entirely invented in-house utilizing transformers and generative LLMs, and we use its core and personalization capabilities to power everything from search itself to recommendations to shopping agents. Engineering is by far our largest department, and we’ve built our proprietary engine to be the best on the market, having never lost an A/B test to a competitive technology. We’re passionate about maintaining this and work on the bleeding edge of AI to do so.</p><p>Out of necessity, our engine is built for extreme scale and powers over 1 billion queries every day across 150 languages and roughly 100 countries. It is used by some of the biggest ecommerce companies in the world like Sephora, Under Armour, and Petco.</p><p>We’re a passionate team who love solving problems and want to make our customers’ and coworkers’ lives better. We value empathy, openness, curiosity, continuous improvement, and are excited by metrics that matter. We believe that empowering everyone in a company to do what they do best can lead to great things.</p><p>Constructor is a U.S. based company that has been in the market since 2019. It was founded by Eli Finkelshteyn and Dan McCormick who still lead the company today.</p><p><strong>Constructor</strong> is seeking an experienced <strong>AI Engineer</strong> to design and build our <strong>Agent Products</strong>. In this role, you will focus on developing and rigorously evaluating sophisticated <strong>RAG pipelines</strong> and <strong>agentic workflows</strong> that power new ways of shopping. Our <strong>LLM-powered agents</strong> utilize various tools to interact with customers’ catalogs, enable advanced retrieval, and assist with browsing to answer open-ended user queries. These agents provide high-quality, well-cited answers and deliver exceptional product recommendations.</p><h3>Responsibilities</h3><ul> <li>Architect and build real-time agentic workflows to handle complex, multi-step user tasks and open-ended queries, providing users with accurate and contextually relevant answers and product suggestions </li> <li>Own the end-to-end data lifecycle for AI workflows, including vector database ingestion and indexing </li> <li>Design metrics to evaluate the relevance and performance of query results, ensuring alignment with business goals and user expectations </li> <li>Generate and rapidly prototype novel product hypotheses that leverage LLMs, RAG, and agentic systems </li> <li>Collaborate closely with Product, Design, Analytics, and other engineering teams to translate AI capabilities into tangible, high-quality product features </li> <li>Improve the speed, quality, and efficiency of our AI systems and engineering processes </li> <li>Take ownership of systems and designs from conception through to deployment and maintenance </li> </ul><p></p><h3>Qualifications</h3><ul> <li>4+ years of industry experience in related fields, including search, information retrieval, recommendation systems, applied machine learning, and NLP </li> <li>Excellent skills in delivering and communicating business value </li> <li>Proficient in Python, SQL, and the big data stack for end-to-end ML product development, with experience across the entire pipeline in typical recommendation systems or LLM-based solutions </li> <li>Strong grasp of Information Retrieval (IR) techniques (e.g., dense retrieval, re-ranking, chunking strategies) </li> <li>Direct experience with <strong>Retrieval-Augmented Generation (RAG)</strong>; experience building <strong>autonomous agents</strong> is a strong plus </li> <li>Nice to have: experience with <strong>automatic prompt optimization</strong> techniques (e.g., DSPy) </li> <li>Solid understanding of ML evaluation methodologies and key IR metrics </li> <li>Passion for shipping high-quality products and a self-motivated drive to take ownership of tasks </li> </ul><p></p><h3>Tech Stack</h3><ul> <li> <strong>Core</strong>: Python, FastAPI, asyncio, Airflow, Luigi, PySpark, Docker, LangGraph </li> <li> <strong>Data Stores</strong>: Vector Databases, DynamoDB, AWS S3, AWS RDS </li> <li> <strong>Cloud &amp; MLOps</strong>: AWS, Databricks, Ray </li> </ul>