Senior AI Engineer/ A2A, Databricks, MCP

Responsibilities

• Participate in use case deep-dive sessions and translate business requirements into specifications using a 15-characteristic framework covering scope, tools, memory, guardrails, evaluation criteria, and acceptance definition
• Design and implement the RAG pipeline, including document retrieval, chunking strategy, embedding, reranking, and response generation
• Integrate agents with platform MCP servers for tool access and with the LLM Gateway for model routing
• Implement prompt engineering practices such as system prompts, few-shot examples, and chain-of-thought patterns, and iterate based on evaluation results
• Wire guardrails, including prompt injection protection and domain boundary enforcement, for reference agents
• Run evaluation cycles using the platform Evaluation Framework, including LLM-as-judge scoring and RAG faithfulness and relevance metrics, and iterate until quality gate criteria are met
• Collaborate with the Data Engineer on data schema and retrieval interface design, and with the QA Engineer on test coverage and the acceptance test query set
• Support UAT with business stakeholders, address feedback, and prepare agents for production deployment following the platform runbook

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