Ai Engineer

Role: Data & Platform Engineering ( Azure, Python) Duration: 2 Months Work Timings: French Timings Years of Experience: 5+ years Key Responsibilities Design and develop scalable data and analytics solutions using Microsoft Fabric and Azure services Build and maintain data pipelines using Python, Fabric Data Engineering, Lakehouse, Notebooks, and Pipelines Integrate analytical and ML outputs into downstream analytics platforms and dashboards Implement data transformations, validation checks, and performance optimisations Predictive Modeling & Analytics Design, build, and iterate on predictive and analytical models using Python Develop models and scoring logic that support real-world decision-making, with an emphasis on interpretability and stability Produce explainable outputs, including scores, categories, and key drivers influencing model results Perform diagnostics and validation to understand model behaviour across different data segment. Decision Support & Scenario Analysis Translate business rules, constraints, and policies into quantitative logic that can be applied consistently Build what-if and scenario analyses to evaluate trade-offs and outcomes under changing assumptions Clearly communicate model behaviour, limitations, and trade-offs to non-technical stakeholders www.crmit.com Follow Us On: Collaboration & Governance Work closely with business stakeholders to understand decision processes and analytical needs Collaborate with platform and architecture teams to productionize solutions responsibly Contribute to technical design discussions and ensure adherence to security, governance, and compliance standards Troubleshoot production issues and continuously improve solution reliability and clarity Required Skills Solid experience with Microsoft Azure, including services such as Azure Data Factory / Synapse / Azure Functions, Azure Data Lake Storage (ADLS), Azure SQL (Cosmos DB is a plus) Hands-on experience with Microsoft Fabric, including Data Engineering, Lakehouses, Notebooks, and Pipelines Understanding of data modeling, ETL/ELT patterns, and analytics workloads Strong proficiency in Python, including data processing, feature engineering, and ML workflows Experience building predictive or analytical models using libraries such as scikit-learn, pandas, NumPy Experience with Git, CI/CD pipelines, and DevOps practices Ability to explain analytical concepts and results clearly to non-technical stakeholders Good to Have Experience with model interpretability or explainability techniques Exposure to scenario analysis, simulation, or decision-support systems Experience with Azure Machine Learning or ML deployment Knowledge of Power BI and semantic models in Fabric www.crmit.com Follow Us On: Understanding of data quality, bias, or fairness considerations Familiarity with MLOps concepts