Machine Learning Artificial Intelligence Engineer

City National Bank of Florida is seeking a hands‑on Machine Learning / AI Engineer to develop enterprise‑grade AI applications, retrieval‑augmented generation (RAG) systems, and agentic AI workflows. The engineer will work on real banking use cases involving financial document analysis, credit writing, enterprise knowledge retrieval, risk and fraud prevention, and employee productivity.

The ideal candidate combines strong Python software engineering skills with practical experience building, testing, and deploying LLM‑powered applications. This is a development‑focused role, not a pure research or experimentation position.

What You’ll Do:

• Design, develop, test, and maintain production‑quality Python applications for generative AI and applied machine learning.
• Build RAG pipelines using document ingestion, parsing, chunking, embeddings, metadata filtering, hybrid search, reranking, and grounded response generation.
• Develop AI agents and workflow components using Lang Chain v1 and Lang Graph.
• Integrate Azure OpenAI models and Azure AI Search into enterprise AI applications.
• Implement structured outputs using Pydantic, schema validation, deterministic business rules, and robust error handling.
• Build document validation, financial data extraction, summarization, classification, and analysis components.
• Implement automated evaluation datasets and tests to measure retrieval quality, answer correctness, grounding, and regression.
• Develop REST APIs and backend services using FastAPI or an equivalent Python framework.
• Write unit tests, integration tests, and mock‑based tests; participate in code reviews and continuous integration.
• Implement secure handling of sensitive banking information, prompt‑injection defenses, input validation, and appropriate AI guardrails.
• Deploy containerized applications and contribute to Azure monitoring, logging, troubleshooting, and performance optimization.
• Collaborate with senior engineers, AI leadership, business stakeholders, and risk and compliance teams.

• 4–5 years of professional software development experience, with strong hands‑on Python proficiency.
• Strong knowledge of object‑oriented programming, data structures, algorithms, modular design, and clean‑code principles.
• Experience with FastAPI, REST APIs, Pydantic, SQL, Git, pytest, and debugging.

• Practical experience building LLM‑powered applications using OpenAI, Azure OpenAI, or comparable model APIs.
• Understanding of transformers, embeddings, tokenization, semantic search, and model inference.
• Hands‑on experience with RAG, including document ingestion, chunking strategies, retrieval, grounding, and evaluation.
• Experience with Lang Chain, Lang Graph, or comparable orchestration frameworks. Direct Lang Graph experience is strongly preferred.
• Experience with prompt design, structured generation, function/tool calling, and output validation.
• Ability to measure and improve AI quality rather than relying solely on subjective demonstrations.

• Experience with Azure or another major cloud platform;
  Azure experience is preferred.
• Familiarity with Docker, CI/CD, application logging, monitoring, and automated testing.
• Ability to troubleshoot API failures, retrieval defects, latency, token usage, and model‑related errors.

• Experience with Azure AI Search, hybrid search, reranking, or vector databases.
• Experience building AI agents that call enterprise APIs and tools.
• Familiarity with Model Context Protocol (MCP).
• Experience with PostgreSQL, Azure Blob Storage, or document‑processing pipelines.
• Experience in banking, financial services, lending, fraud, or another regulated industry.
• Familiarity with…