Databricks AI Engineer

Databricks AI Engineer

Job Summary

The Databricks AI Engineer builds AI capability that runs in production rather than in a notebook. This role covers retrieval augmented generation, agent design, model serving, evaluation, and the data engineering underneath all of it. The engineers who succeed here treat AI systems as software systems, with tests, monitoring, versioning, and a clear definition of what good output looks like.

Key Responsibilities

• Design and build retrieval augmented generation applications using Mosaic AI Vector Search and foundation model APIs

• Build and orchestrate agent workflows including tool calling, function execution, and multi step reasoning

• Develop the chunking, embedding, and indexing pipelines that determine retrieval quality

• Deploy models and endpoints through Mosaic AI Model Serving with appropriate scaling and rate limiting

• Build evaluation harnesses that measure accuracy, groundedness, latency, and cost, and track them over releases

• Implement guardrails, prompt management, and human review loops for high risk outputs

• Fine tune or adapt open models where the use case justifies it

• Partner with data governance so AI applications respect the same access controls as the underlying data

Required Skills

• 5+ years in software or data engineering, with 2+ years building AI or ML applications

• Strong Python and hands on experience with an LLM application framework such as LangChain, LlamaIndex, or DSPy

• Practical retrieval augmented generation experience including chunking strategy and vector search tuning

• Experience deploying and monitoring model endpoints in production

• Understanding of evaluation methodology for generative systems and the limits of it

• Working knowledge of MLflow for tracking, versioning, and model registry

Preferred Skills

• Databricks Certified Generative AI Engineer Associate or Machine Learning Professional

• Hands on Mosaic AI experience including Agent Framework and Agent Evaluation

• Fine tuning experience with LoRA or similar parameter efficient methods

• Experience with AI governance, red teaming, or model risk management

• Prior delivery of a customer facing AI application at enterprise scale

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