Data Engineering & AI Platform Engineer - Quality

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.

• 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.

• 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).

• 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 .

• 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.