AI Systems Engineer
Insight Global
This large transportation company is seeking an AI Engineer to join our Service Analytics organization, supporting rail optimization, service analytics, and shipper decisioning. In this role, you will use Claude and modern AI tooling to build data pipelines, accelerate data science work, and ship production AI capabilities on top of our Azure and Databricks platform. You will sit at the intersection of data engineering and data science. You will build data pipelines and LLM-powered systems, but what amplifies this role beyond a standard DS/DE seat is your fluency with Claude as a development partner: Claude Code, custom Skills, sub-agents, hooks, and the Model Context Protocol (MCP). A core pillar of this work is Retrieval-Augmented Generation (RAG). You will design and operate RAG pipelines that ground LLM outputs in our operational and telematics data, turning raw signal into reliable, cited answers for operators and shippers. This means hands-on ownership of the full retrieval stack: chunking and embedding strategies, vector index design, hybrid search (dense + sparse), re-ranking, and context assembly. You will work with vector databases (Azure AI Search, Databricks Vector Search, or equivalent) to power semantic search across maintenance records, route histories, and equipment documentation. You will evaluate retrieval quality rigorously measuring recall, relevance, and answer faithfulness and iterate on both the retrieval and generation layers to close gaps.
Submit your resume along with a link to a public GitHub repository containing a RAG pipeline you've built. This role will be onsite 4 days a week.
Required Skills & Experience Bachelor's degree in Computer Science, Data Science, Information Management, or a related field (Master's preferred)
Hands-on experience with Claude Code - Skills, sub-agents, hooks, MCP servers, and settings - and how to compose them into reliable engineering workflows; we want to see what you've built, not just familiarity
Demonstrated experience building production RAG systems: owning the retrieval stack end-to-end including embedding models, vector store configuration, hybrid search, and retrieval evaluation
Hands-on with at least one vector database: Azure AI Search, Databricks Vector Search, Pinecone, Weaviate, Qdrant, or similar
Databricks experience in data engineering or data science contexts
Strong Python and SQL fundamentals
Nice to Have Skills & Experience VS Code
Antigravity
Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.
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