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Mid-level AI/ML role with common required skills and 4–8 years experience, attracting moderate competition.
Skills are technical and transferable across industries but Databricks/RAG specialization raises moderate domain sensitivity.
Multiple mandatory technical requirements (Databricks, RAG, LLMs, Python, SQL) plus explicit 4–8 years, increasing filter strictness.
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Own development and maintenance of ETL/ELT data pipelines using Databricks.
Build and implement AI applications based on Retrieval-Augmented Generation (RAG) architecture.
Integrate and optimize AI/ML models and data solutions involving LLMs, embeddings, and vector databases for performance and scalability.
4-8 years of experience in AI/ML engineering or related field.
Strong hands-on experience in Python programming.
Proven experience with Databricks and ETL/Data Engineering.
Hands-on expertise with RAG architecture, LLM applications, SQL, and data processing.
Experienced in building scalable AI solutions involving RAG, embeddings, vector databases, and prompt engineering.
Familiar with integrating AI models within enterprise data platforms and applications.
Has knowledge of cloud platforms (Azure/AWS/GCP) and frameworks like LangChain or LlamaIndex, enhancing deployment and model management.