AI & Data Engineer
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EliteAI-generated questions, company research, and talking points tailored to this role
Prepare for this interview
EliteAI-generated questions, company research, and talking points tailored to this role
We are looking for a Data & AI Engineer to join our Data, AI & Integrations team. In this role, you will design and build generative AI applications and data pipelines that help teams across AutoStore work smarter, faster, and more efficiently. From automating account planning to transforming complex internal and external data into actionable insights, your work will directly enable people and systems to make better decisions and focus on what matters most. Job Responsibilities: Design, build, and deploy generative AI applications, including chatbots, AI agents, knowledge retrieval tools, and RAG systems, with LLM orchestration and AI pipelines. Build, optimize, and maintain data pipelines and document/vector search processing for AI and analytics use cases, leveraging Delta Lake, PySpark, and Databricks. Integrate generative AI capabilities into business tools and applications, such as Microsoft Teams and web platforms. Implement monitoring, logging, evaluation, and observability to ensure reliable and performant AI applications. Transform complex structured and unstructured data into actionable insights to support teams across the organization. Job Requirements: 1-3 years of experience in Data Engineering, Machine Learning, or Generative AI applications; recent graduates with relevant skills are welcome. University degree in Computer Science, Data Science, Software Engineering, AI/ML, or a related field. Proficient in Python and applies software engineering best practices. Experience or interest in building applications with LLMs and AI agent frameworks (e.g., LangGraph, OpenAI Agents SDK, Pydantic AI), including RAG architecture, chunking strategies, embedding models, and vector search. Strong data engineering skills, including building pipelines, data products, and models for structured/semi-structured data, with experience in PySpark, cloud platforms (e.g., Azure Databricks), and a good understanding of machine learning concepts such as model training, evaluation, and deployment.
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