SENIOR DATA ENGINEER
TE Connectivity · Bengaluru · onsite · Posted 1d ago
Undisclosed5–10 yrsfulltimeSourced from
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About this role
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
AI Data Engineering Architecture
- Design and develop scalable data pipelines to prepare structured, semi-structured, and unstructured enterprise data for AI/GenAI applications.
- Build standardized, reusable, and governed AI-ready datasets.
- Implement data ingestion, transformation, validation, and refresh mechanisms supporting AI applications.
RAG, Embeddings Vector Data Engineering
- Develop data pipelines supporting Retrieval-Augmented Generation (RAG) solutions.
- Implement document ingestion, parsing, text extraction, cleaning, chunking, and metadata enrichment.
- Develop and maintain embedding generation, refresh, and indexing workflows.
Knowledge Graph Graph Data Engineering
- Design and develop graph-ready datasets representing enterprise entities and relationships.
- Transform relational and other enterprise data sources into nodes, relationships, and properties.
- Support knowledge graph development and graph-based data retrieval.
Cloud AI Data Integration
- Build secure data integrations across Azure, Databricks, enterprise databases, cloud storage, and APIs.
What your background should look like
- Strong hands-on experience in Python and SQL, including data processing, API integration, and reusable engineering solutions.
- Experience developing scalable data pipelines using Databricks, PySpark, or equivalent cloud data engineering platforms.
- Practical experience preparing structured, semi-structured, and unstructured data for AI applications.
- Experience with document ingestion, parsing, text extraction, chunking, and metadata engineering.
- Working knowledge of RAG architecture, embeddings, vector search, and semantic retrieval.
- Familiarity with vector databases or search technologies such as Azure AI Search, FAISS, Chroma, Pinecone, or equivalent.
- Experience integrating enterprise databases, REST APIs, and cloud storage platforms.
- Understanding of AI data quality, security, governance, and production monitoring.
- Familiarity with Git, CI/CD, and modern software engineering practices.
Competencies
- Values: Integrity, Accountability, Inclusion, Innovation, Teamwork
Sourced from naukri
Skills
Data EngineerData ingestioncloud data engineeringAzure Aiapiscloud aiCloud StorageAnalyticsData EngineeringcloudArchitectureData IntegrationAdvanced AnalyticsMicrosoft AzureAPI IntegrationArtificial Intelligence
About TE Connectivity
TE Connectivity
Electronics Manufacturing
1.2k+
employees
1941
85 years old
Bangalore / Bengaluru, Karnataka
India
About
TE Connectivity specializes in connectivity and sensing solutions, emphasizing sustainability and reliability to address the needs of various industries.
Industries
Electronics ManufacturingInternetTelecomElectronics
Employee ratings
1,327 reviews
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Culture
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