Data Engineering & AI Platform Engineer - Quality

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.

Cummins is an equal opportunity employer. Our policy is to provide equal employment opportunities to all qualified persons without regard to race, sex, color, disability, national origin, age, religion, union affiliation, sexual orientation, veteran status, citizenship, gender identity, or other status protected by law.
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