AI Data Engineer

Responsibilities

Build and optimize ETL pipelines for large-scale AI applications, integrating APIs, web scraping, and real-time data processing. Develop and maintain scalable AI data infrastructure using PySpark, Pandas, SQL, and cloud services (Azure, AWS, GCP). Implement retrieval-augmented generation (RAG) pipelines using FAISS, ChromaDB, and Pinecone for AI-driven insights. Deploy and monitor AI applications using FastAPI, Streamlit, Docker, for real-time performance. Work with cross-functional teams to ensure data security, compliance, and AI model reliability in production environments. Qualifications

Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence, or a related field. Atleast 2-4 years of experience in data engineering, AI / ML, or cloud-based AI infrastructure. Expertise in Python, PySpark, and SQL for data transformation and large-scale processing. Experience with cloud platforms (AWS, Azure, GCP) for AI model deployment and data pipeline automation. Hands-on experience with vector databases (FAISS, ChromaDB, Pinecone) for efficient data retrieval. Proficiency in containerization and orchestration tools like Docker and Kubernetes. Strong understanding of retrieval-augmented generation (RAG) and real-time AI model deployment. Knowledge of API development and AI service integration using FastAPI and Streamlit / Dash. Ability to optimize AI-driven automation processes and ensure model efficiency. Strong analytical and problem-solving skills. Excellent communication and collaboration abilities.

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