AI Data Engineer

Overview We are seeking an experienced AI Data Engineer (15+ Years) to design, develop, and manage scalable data platforms that enable advanced analytics, Machine Learning (ML), and Generative AI solutions. The ideal candidate will build robust data pipelines, ensure data quality, and integrate AI/ML capabilities into enterprise data ecosystems. Mandatory Skills : Python, AI/ML. Retail domain experience is highly preferable. Key Responsibilities Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data. Build and optimize data lakes, data warehouses, and AI-ready data platforms. Develop ingestion, transformation, and orchestration frameworks using cloud-native technologies. Prepare, cleanse, and engineer datasets for AI/ML and Generative AI workloads. Integrate Large Language Models (LLMs), vector databases, embeddings, and Retrieval-Augmented Generation (RAG) pipelines into enterprise solutions. Implement data governance, security, lineage, and quality controls. Collaborate with Data Scientists, AI Engineers, Business Analysts, and Solution Architects. Monitor, troubleshoot, and optimize data pipelines and platform performance. Automate deployment, testing, and monitoring of data engineering workflows. Create technical documentation and data dictionaries for enterprise data assets. Required Skills Technical Skills Python, SQL, PySpark ETL/ELT development Data Modeling (Star Schema, Snowflake Schema) Apache Spark, Databricks Airflow, Dataflow, Informatica, ADF, Synapse, or equivalent tools Relational & NoSQL Databases Data Warehousing concepts REST APIs and Microservices Git, CI/CD, DevOps practices AI & GenAI Skills Machine Learning fundamentals Data preparation for AI models Vector Databases (Pinecone, ChromaDB, FAISS) LLM Integration (OpenAI, Azure OpenAI, Gemini, Claude, etc.) RAG Architecture Embeddings and Semantic Search Prompt Engineering fundamentals #J-18808-Ljbffr