Data and AI Engineer

Design, build, and maintain scalable ETL/ELT pipelines to ingest structured and unstructured data from SAP S/4HANA, Oracle, Salesforce, and third-party APIs into enterprise data lakes/lakehouses.

Develop and optimize data products (medallion architecture) on platforms like Databricks Implement data quality, lineage, and governance controls using tools such as Purview, Alation, or Unity Catalog.

Build, deploy, and monitor ML models (classification, regression, forecasting, anomaly detection) using Python, PySpark, scikit-learn, TensorFlow, or PyTorch.

Develop GenAI and LLM-powered solutions — including RAG (Retrieval-Augmented Generation) pipelines, embeddings, vector databases (Pinecone, Azure AI Search), and prompt engineering for Copilot/Azure OpenAI/Bedrock.

Operationalize models through MLOps practices — CI/CD for ML, model versioning, drift monitoring, and automated retraining (MLflow, Azure ML, SageMaker, Vertex AI) Deploy and manage data/AI workloads on Azure, AWS, or GCP , leveraging services like ADF, Databricks, Glue, Lambda, Functions, and Kubernetes.

4–6 years of professional experience in data engineering, ML engineering, or applied AI roles.

Proven track record of delivering at least 2–3 production-grade data or AI projects end-to-end Working in global team ecosystem is a plus Preferred Qualifications: Experience with enterprise ERP data (SAP S/4HANA, Oracle) and integration patterns (CDC, IDoc, OData, APIs).

Exposure to supply chain, procurement, Logistics analytics use cases (spend, demand forecasting, supplier risk).

Familiarity with Copilot Studio, or Agentic AI frameworks .