AI Data Engineer

Qualifications & Experience

• 4–10 years of professional experience in AI/ML, Data Science, Data Engineering, Software Engineering, or related fields.
• Strong hands-on experience with AI/ML model development, data engineering, and analytical problem solving.
• Strong proficiency in Python, SQL, and PySpark.
• Hands-on experience with Databricks and large-scale data processing.
• Strong understanding of machine learning concepts, including supervised, unsupervised, and reinforcement learning.
• Experience designing and developing ETL/ELT pipelines and data processing workflows.
• Experience working with large-scale, structured and unstructured datasets.
• Strong understanding of data modeling, data integration, and unified data layers across multiple data sources.
• Experience developing and deploying AI/ML models into production environments.
• Experience building APIs and integrating AI/ML solutions with existing software applications and enterprise systems.
• Hands-on experience or strong familiarity with LLM applications, RAG, AI agents, tool/function calling, and agentic workflows.
• Experience with AI/LLM frameworks such as LangChain, LangGraph, AutoGen, or equivalent.
• Strong understanding of MLOps concepts, including model deployment, monitoring, optimization, and data/model drift.
• Strong analytical and problem-solving skills with the ability to convert business problems into measurable AI solutions.
• Strong communication skills with experience working with business stakeholders, technical teams, and senior leadership.