**DESCRIPTION** Design, build, and maintain scalable data and AI platforms, including graph-based infrastructure, to enable document intelligence, multimodal information retrieval, and AI-driven validation pipelines across enterprise environments. **Key Responsibilities** + Design and implement scalable data engineering and AI platforms for enterprise environments. + Develop and optimize pipelines for document intelligence, multimodal retrieval, and AI-driven validation. + Build and manage vector database solutions and embedding-based search workflows. + Architect and maintain graph-based systems using Neo4j and GraphRAG for advanced knowledge representation. + Ensure robust MLOps practices, including model lifecycle management and data governance. + Collaborate with cross-functional teams to integrate RAG frameworks and multimodal data sources. + Automate data orchestration and visualization using Python and Streamlit. **RESPONSIBILITIES** **Core Competencies:** + **Collaborates:** Builds strong partnerships and works effectively with others to meet shared objectives. + **Communicates Effectively:** Conveys information clearly across diverse audiences. + **Customer Focus:** Develops strong relationships and delivers customer-centric data solutions. + **Interpersonal Savvy:** Engages openly with diverse teams and perspectives. + **Data Analytics, Mining & Modeling:** Extracts insights, builds models, and drives data-driven decision-making. + **Data Communication & Visualization:** Tells a compelling story through data visuals and narratives. + **Data Literacy & Quality:** Ensures reliable, high-quality data across analytics ecosystems. + **Values Differences:** Recognizes and leverages diverse perspectives for innovation and growth. **Skills & Technical Expertise** + **Programming & Automation:** Advanced Python for data orchestration, automation, and AI workflows; Streamlit for interactive dashboards. + **Data Engineering:** Hands-on experience with Databricks, PySpark, and Azure Data Lake for scalable ETL and data transformation. + **AI & ML:** Expertise in RAG frameworks, multimodal data integration, and embedding pipelines for semantic search. + **Graph Technologies:** Advanced Neo4j graph modeling and GraphRAG for knowledge representation and retrieval. + **Vector Databases:** Building and managing embedding-based search solutions. + **MLOps & Governance:** End-to-end model lifecycle management, deployment, monitoring, and compliance. + **Problem-Solving & Communication:** Strong analytical thinking and ability to collaborate with cross-functional teams. + **Domain Knowledge:** Understanding of manufacturing quality processes (preferred). **QUALIFICATIONS** **Experience:** + **3–6 years** of relevant experience in **data engineering, AI application development, and deployment** . + Proven hands-on experience in **building AI, data, and graph infrastructure** across enterprise environments. + Experience in **data governance** , **model lifecycle management** , and **enterprise analytics enablement** . **Qualifications:** + Bachelor’s or Master’s degree in **Computer Science, Information Technology** , or a **related technical discipline** . + This position may require licensing for compliance with export controls or sanctions regulations. Role Category - **On-site with Flexibility** Dayshift **Job** Quality **Organization** Cummins Inc. **Role Category** Off-site Remote **Job Type** Exempt - Experienced **ReqID** 2421966 **Relocation Package** No **100% On-Site** No