Data Engineer (AI/ML)Engineer

Key Responsibilities • Develop and deploy AI/ML models using Python, Scikit-learn, TensorFlow, or PyTorch. • Build ML solutions for classification, NLP, anomaly detection, forecasting, and other enterprise AI use cases. • Design and implement scalable data pipelines using Databricks, PySpark, Delta, MLflow, and Azure Data Factory. • Work with SAP data models, SAP BDC, SAP Analytics Cloud, and SAP data migration/transformation workflows. • Collaborate with business stakeholders to translate business requirements into AI-driven technical solutions. • Implement MLOps practices including model validation, monitoring, retraining, and performance tracking. • Support PoCs, prototypes, and rapid experimentation cycles for new AI and SAP-related use cases. • Work with cross-functional teams across data engineering, SAP, cloud, business, and DevOps teams. Must-Have Skills • AI/ML & Programming: Strong hands-on AI/ML development experience using Python, Scikit-learn, TensorFlow, or PyTorch. • Model Development: Experience building and deploying ML models for classification, NLP, anomaly detection, and similar use cases. • Data Engineering: Hands-on experience with Databricks, PySpark, Delta, and MLflow. • ETL/ELT Pipelines: Practical experience with Azure Data Factory for data ingestion, transformation, and orchestration. • SAP & Data Integration: Experience with SAP BDC, SAP Analytics Cloud, SAP data models, and SAP data integration concepts. • SAP Migration: Exposure to SAP data migration, transformation, cleansing, and validation workflows. • Cloud & MLOps: Familiarity with Azure ML or similar ML platforms, along with MLOps practices such as model monitoring, retraining, and validation. • Core Competencies: Strong problem-solving ability in complex data environments, good communication skills, and cross-team collaboration. Nice-to-Have Skills • Bachelor’s degree in Computer Science, Artificial Intelligence, Data Engineering, or a related field. • Experience in SAP data migration programs or enterprise transformation initiatives. • Hands-on exposure to Azure DevOps, CI/CD pipelines, and automated deployment practices. • Experience working in large, cross-functional organizations with distributed teams. • Ability to work in ambiguous environments, drive PoCs, and support rapid prototyping cycles. Preferred Candidate Profile The ideal candidate should have a strong blend of AI/ML development, data engineering, SAP data understanding, and cloud/MLOps exposure. The candidate should be capable of understanding business problems, identifying suitable AI approaches, building scalable data pipelines, and delivering production-ready AI solutions in enterprise environments.