MOURIT Ech is seeking an innovative professional with deep expertise in Artificial Intelligence (AI) and Machine Learning (ML) to elevate our enterprise Data Governance program. This role will design and implement AI-driven solutions to automate and enhance data governance processes from intelligent data cataloging and metadata curation to data quality monitoring and compliance checks while ensuring security, reliability, and responsible AI practices. The AI Engineer will collaborate closely with Data Governance, Data Engineering, and Product teams to integrate advanced AI capabilities (LLMs, NLP, etc.) into our data platforms, thus driving greater efficiency, consistency, and value from our data assets across the organization.

Key Responsibilities :

- AI Solution Engineering for Data Governance : Design, build, and deploy AI/ML solutions leveraging large language models (LLMs), natural language processing (NLP), and other advanced techniques to automate metadata management, data classification, lineage documentation, and data quality processes in our governance platforms (e.g. Microsoft Purview, Databricks Unity Catalog). Translate data governance challenges into technical strategies and model architectures that improve data catalog completeness, quality, and compliance.

- Model Development & Optimization : Develop and train machine learning models (e.g. classification models for sensitive data detection, anomaly detection models for data quality). Iterate on model performance through experiments, hyperparameter tuning, and evaluation; document results and refine models to maximize accuracy and efficiency in the data governance context.

- Integration & ML Operations : Work with Data Engineering and ML Ops teams to operationalize AI models and analytics pipelines. Implement continuous integration/continuous deployment (CI/CD) workflows and use model registries (e.g. Azure Machine Learning, MLflow) to deploy and monitor models in production. Develop APIs or automated workflows that integrate model outputs into existing data governance processes and tools (for example, updating Microsoft Purview metadata or triggering Power Automate workflows for data quality issues). Ensure deployed solutions meet performance, scalability, and latency requirements within our Azure-based data environment.

- Governance & Responsible AI : Embed HGVs data governance and compliance standards into all AI solutions. Conduct bias assessments and ensure explainability for AI models that support governance decisions. Maintain thorough documentation for AI models and their governance (e.g. model lineage, usage policies, risk assessments). Ensure all solutions comply with data privacy regulations and internal security policies, and champion responsible AI best practices in the handling of enterprise data.

- Cross-Functional Collaboration : Partner with Data Governance leadership, Data Architects, Product Operations, and business stakeholders to identify high-impact opportunities for AI automation. Provide thought leadership on how AI can solve data governance challenges and improve processes. Communicate complex technical concepts and model findings in clear business terms, enabling informed decision-making and broad adoption of AI-driven governance solutions.

Technical Qualifications :

- Experience : 5 to 8+ years of hands-on experience in AI/ML engineering, data science, or data engineering, including successfully deploying machine learning solutions into production. Exposure to data governance or data management initiatives is highly valuable.

- Programming & ML Expertise : Strong programming skills in Python and experience with ML frameworks. Solid software engineering practices (code versioning, testing, CI/CD) for maintaining robust and reusable code.

- AI/ML Skills : Demonstrated expertise in NLP and working with large language models (LLMs) to analyze or generate text. Familiarity with developing generative AI solutions and prompt engineering is a plus. Skilled in building and tuning ML models for tasks such as classification, prediction, anomaly detection, and information extraction.

- Cloud & MLOps : Experience deploying ML models in cloud environments (Azure preferred). Hands-on knowledge of ML Ops tools and practices e.g. using Azure Machine Learning or Databricks for model training, MLflow or similar for model tracking, and implementing pipelines for continuous model integration and delivery.

- Data Governance Tools : Working knowledge of enterprise data catalog and metadata management systems. Experience with Microsoft Purview and Databricks Unity Catalog (or similar data cataloging tools) is strongly preferred ability to utilize these platforms for metadata curation, lineage capture, and integration of AI-driven enhancements. Proficiency with analytics and productivity tools such as Power BI (for governance dashboards) and Power Automate (for workflow automation) is a plus.

- Data Governance Domain : Solid understanding of data governance and data management principles including metadata standards, data lineage, data quality, privacy and compliance requirements (e.g. handling of sensitive data, retention policies). Ability to incorporate these principles into AI solutions to ensure they meet enterprise governance objectives.

Desired Skills :

- Advanced Tooling : Familiarity with advanced AI techniques and tools, such as building retrieval-augmented generation (RAG) systems, using vector databases for semantic search, or deploying chatbots/AI assistants to aid in data discovery and documentation.

- Enterprise Systems Integration : Experience integrating AI solutions into enterprise ecosystems, for example linking AI outputs with SharePoint, Teams, or other collaboration and record-keeping systems to support governance workflows and documentation.

- Communication & Leadership : Excellent communication skills with the ability to explain complex AI concepts and insights to non-technical stakeholders. Experience providing technical mentorship or leadership in a cross-functional setting is advantageous.

- Industry Knowledge : Understanding of regulatory frameworks and industry best practices related to data governance and AI (e.g. data privacy laws, AI ethics guidelines). Prior experience aligning AI initiatives with frameworks for data privacy, records management, or compliance (such as DAMA DMBOK, GDPR/CCPA, etc.) is a plus.

Education :

- Bachelors degree in Computer Science, Data Science, Information Systems, Engineering, or a related field is required. A Masters degree in a relevant discipline is a plus. Equivalent practical experience in enterprise data governance and machine learning can be considered.

Certifications :

- Preferred certifications include Microsoft Azure AI Engineer Associate, Azure Data Engineer, or other relevant AI/ML certifications. Certifications in data governance or data management (such as DAMA Certified Data Management Professional), and vendor-specific credentials for tools like Microsoft Purview or the Power Platform, are also valued.