At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world.
Job Overview
We are seeking a skilled
AI Data Engineer
to design, develop, and maintain scalable, secure, and production-ready data solutions that enable enterprise Artificial Intelligence (AI), Generative AI (GenAI), and advanced analytics applications.
This role will focus on engineering
AI-ready datasets, unstructured data pipelines, Retrieval-Augmented Generation (RAG) data pipelines, embedding workflows, vector search solutions, and knowledge graph data structures
to support enterprise AI initiatives.
The successful candidate will work with technologies such as
Python, SQL, Databricks, PySpark, Azure, cloud storage, APIs, vector databases, and modern AI data frameworks
, collaborating with AI/ML teams, Data Engineering teams, and business stakeholders.
The role requires hands-on AI data engineering capabilities, strong programming fundamentals, and the ability to transform enterprise data into reliable, governed, and reusable assets for AI applications.
Job Responsibilities
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AI Data Engineering & Architecture
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Design and develop scalable data pipelines to prepare structured, semi-structured, and unstructured enterprise data for AI/GenAI applications.
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Build standardized, reusable, and governed AI-ready datasets.
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Implement data ingestion, transformation, validation, and refresh mechanisms supporting AI applications.
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RAG, Embeddings & Vector Data Engineering
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Develop data pipelines supporting Retrieval-Augmented Generation (RAG) solutions.
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Implement document ingestion, parsing, text extraction, cleaning, chunking, and metadata enrichment.
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Develop and maintain embedding generation, refresh, and indexing workflows.
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Knowledge Graph & Graph Data Engineering
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Design and develop graph-ready datasets representing enterprise entities and relationships.
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Transform relational and other enterprise data sources into nodes, relationships, and properties.
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Support knowledge graph development and graph-based data retrieval.
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Cloud AI Data Integration
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Build secure data integrations across Azure, Databricks, enterprise databases, cloud storage, and APIs.
What Your Background Should Look Like
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Strong hands-on experience in Python and SQL, including data processing, API integration, and reusable engineering solutions.
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Experience developing scalable data pipelines using Databricks, PySpark, or equivalent cloud data engineering platforms.
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Practical experience preparing structured, semi-structured, and unstructured data for AI applications.
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Experience with document ingestion, parsing, text extraction, chunking, and metadata engineering.
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Working knowledge of RAG architecture, embeddings, vector search, and semantic retrieval.
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Familiarity with vector databases or search technologies such as Azure AI Search, FAISS, Chroma, Pinecone, or equivalent.
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Experience integrating enterprise databases, REST APIs, and cloud storage platforms.
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Understanding of AI data quality, security, governance, and production monitoring.
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Familiarity with Git, CI/CD, and modern software engineering practices.
Competencies
Values: Integrity, Accountability, Inclusion, Innovation, Teamwork
About Te Connectivity
TE Connectivity plc (NYSE: TEL) is a global industrial technology leader creating a safer, sustainable, productive, and connected future. As a trusted innovation partner, our broad range of connectivity and sensor solutions enable the distribution of power, signal and data to advance next-generation transportation, energy networks, automated factories, data centers enabling artificial intelligence, and more.
