Data and AI Engineering Expert
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Key skills for this role
About the Role
ENEC seeks a senior hands-on technical authority to design, develop, and operationalize enterprise data and AI platforms. The role involves leading engineering delivery of scalable data pipelines, AI/ML solutions, and analytics platforms using Databricks, Azure, and industrial data systems.
Key Skills for This Role
Responsibilities
- Design, build, and maintain scalable, secure, and high performance data platforms including Lakehouse architectures using Databricks Delta Lake, Unity Catalog, and Delta Live Tables
- Develop and operationalize robust data pipelines, ETL/ELT workflows, and integration frameworks using Azure Data Factory, Databricks Workflows, and related orchestration tools
- Design, develop, and deploy production grade machine learning, deep learning, and generative AI models using Azure ML, MLflow, and Databricks ML
- Engineer end to end MLOps pipelines covering model training, versioning, validation, deployment, monitoring, and retraining
- Design and enforce data security, access control, and privacy frameworks across all data and AI platforms using Unity Catalog, Collibra, and Azure security controls
Requirements
- Bachelor’s degree in computer science, Information Technology, Data Management, Engineering, or related discipline
- Minimum 12+ years' hands on experience in enterprise data engineering, AI/ML platform development, and cloud native architectures, including production deployment and operations
- Experience embedding governance within modern data platforms (preferably Databricks) and integrating SAP/Oracle ERP domains
- Deep hands on expertise in enterprise data engineering, AI/ML platform development, and cloud native architectures within highly regulated environments
- Expert level proficiency in programming languages and frameworks including Python, SQL, Spark, and relevant AI/ML libraries
Full Job Posting
Role Overview
- The Data and AI Engineering Expert is a senior hands on technical authority responsible for the design, development, and operationalization of enterprise data and AI platforms across ENEC.
- This role leads the engineering delivery of scalable, secure, and compliant data pipelines, AI/ML solutions, and analytics platforms leveraging technologies such as Databricks, Microsoft Azure, Collibra, Power BI, and OT/industrial data systems.
- The Expert applies deep engineering expertise to architect and deliver production grade solutions, ensures data governance and quality standards, and drives adoption of AI and analytics capabilities.
Data Engineering Architecture & Platform Development
- Design, build, and maintain scalable, secure, and high performance data platforms including Lakehouse architectures using Databricks Delta Lake, Unity Catalog, and Delta Live Tables (DLT).
- Develop and operationalize robust data pipelines, ETL/ELT workflows, and integration frameworks using Azure Data Factory, Databricks Workflows, and related orchestration tools.
- Architect and implement data models, semantic layers, and data products that serve enterprise analytics, AI, and reporting needs.
- Ensure seamless integration of OT and industrial data sources — including PI System, SCADA, and DCS — into enterprise data platforms.
- Design and implement APIs, data contracts, and integration frameworks to enable enterprise wide data consumption.
- Ensure platform scalability, resilience, high availability, and disaster recovery capabilities in compliance with nuclear and regulatory requirements.
- Apply and enforce data standards, naming conventions, and metadata management practices across all data assets using Collibra and Unity Catalog.
AI/ML Engineering & Analytics
- Design, develop, and deploy production grade machine learning, deep learning, and generative AI models using Azure ML, MLflow, and Databricks ML.
- Engineer end to end MLOps pipelines covering model training, versioning, validation, deployment, monitoring, and retraining — ensuring reliability and reproducibility.
- Build and operationalize AI powered solutions including predictive analytics, anomaly detection, natural language processing (NLP), and GenAI/LLM based applications using Azure AI Foundry and Copilot Studio.
- Develop BI and semantic models in Microsoft Fabric and Power BI, enabling self service analytics and executive reporting across the organization.
- Apply Responsible AI principles and governance frameworks to all AI and ML solutions, ensuring explainability, fairness, and compliance.
- Conduct rigorous model evaluation, performance benchmarking, and validation to ensure solutions meet accuracy, reliability, and safety standards.
- Contribute to the continuous improvement of AI engineering standards, patterns, and reusable components across the enterprise.
Minimum Qualifications
- Bachelor’s degree in computer science, Information Technology, Data Management, Engineering, or related discipline.
- Minimum 12+ years' hands on experience in enterprise data engineering, AI/ML platform development, and cloud native architectures, including production deployment and operations.
- Experience embedding governance within modern data platforms (preferably Databricks) and integrating SAP/Oracle ERP domains.
Preferred Qualifications
- Master’s degree in a relevant field.
- Experience in energy, utilities, nuclear, financial services, government, or other highly regulated sectors.
- Demonstrated AI governance experience (provenance, bias monitoring, model data requirements) in Databricks or similar AI/analytics platforms exposure.
- Demonstrated experience leading large scale data and AI engineering programs.
Certifications
- Multiple cloud and AI certifications. Microsoft Fabric, Azure AI Foundry, or Databricks Unity Catalog specialty certifications.
- Relevant certifications in one or more of: Databricks (Data Engineer Associate/Professional, ML Professional), Microsoft Azure (Data Engineer, AI Engineer, Solutions Architect), MLOps, or Data Governance (Collibra).
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