Data Engineering & AI Platform Engineer - Quality
Cummins Turbo Technologies
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.
Hybrid role
Dayshift
100% On-Site No
As Cummins continues to grow, you'll be provided with continuous learning opportunities, supportive benefits and a culture that values your wellbeing, safety and work-life balance. Here, you'll have the power to determine your future with innovative technology, a focus on sustainability and with a company positioned for long-term growth.