About the Role
The AI Data Engineer will be responsible for designing, building, and operating scalable data pipelines and curated data assets that power machine learning, generative AI, and intelligent automation solutions in an SLA-driven managed services environment. This role focuses on data ingestion, transformation, governance, and operational reliability across cloud and hybrid environments enabling use cases such as knowledge retrieval (RAG), conversational AI, predictive analytics, and AI-assisted service management. The ideal candidate combines strong data engineering fundamentals with an understanding of AI workload requirements, including quality, lineage, privacy, and performance.
Key Responsibilities
- Design, build, and operate production-grade data pipelines that support AI/ML and generative AI workloads in managed services environments
- Develop curated, analytics-ready datasets and data products to enable model training, grounding, feature generation, and AI search/retrieval
- Implement data ingestion patterns for structured and unstructured sources (APIs, databases, files, event streams, documents)
- Build and maintain transformation workflows with strong testing and validation
- Enable Retrieval-Augmented Generation (RAG) by preparing document corpora, chunking strategies, metadata enrichment, and vector indexing patterns
- Integrate data pipelines with application services
- Support ITSM and enterprise workflow data needs, including ServiceNow data integration, CMDB/incident data quality improvements, and automation enablement
- Implement observability for data pipelines (monitoring, alerting, SLAs/SLOs) and perform root cause analysis for pipeline failures or data quality incidents
- Apply data governance and security best practices
- Collaborate with ML Engineers, DevOps/SRE, and solution architects to operationalize end-to-end AI solutions
- Contribute to reusable patterns, templates, and standards within the Bell Techlogix AI Center of Excellence
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience
- 5+ years of experience in data engineering, analytics engineering, or platform data operations
- Strong proficiency in SQL and Python; experience with data modeling and dimensional concepts
- Hands-on experience with Azure data services (e.g., Data Factory, Synapse, Databricks, Storage, Key Vault) or equivalent cloud tooling
- Experience building reliable pipelines with scheduling, dependency management, and automated testing/validation
- Experience supporting production data platforms with incident management, troubleshooting, and root cause analysis
- Understanding of data security, privacy, and governance principles in enterprise environments
Preferred Qualifications
- Experience enabling AI/ML workloads: feature engineering, training data preparation, and integration with Azure Machine Learning
- Experience with unstructured data processing for generative AI
- Familiarity with vector databases or vector search and RAG patterns
- Experience with event streaming and messaging
- Familiarity with ServiceNow data model and integration patterns (Table API, export, CMDB/ITSM reporting)
- Relevant certifications (Microsoft Azure Data Engineer, Azure AI Engineer, Databricks)