Senior AI Software Engineer — Agentic AI, LangGraph & Azure

City National Bank of Florida•Coral Gables, FL

10d

About The Position

City National Bank of Florida is seeking a hands-on Senior AI Software Engineer to architect, develop, and deploy secure, scalable, production-grade AI applications and agentic workflows. The engineer will take technical ownership of critical AI application components, establish engineering standards, mentor developers through code reviews and design collaboration, and turn business requirements into reliable software. The role combines deep Python engineering with practical experience in LLM orchestration, RAG, APIs, Azure cloud services, testing, security, and production operations. The successful candidate is expected to design solutions and implement them personally. The position includes developing stateful agentic workflows and enterprise RAG systems, integrating Azure and internal services, implementing secure MCP tools, and delivering governed banking AI solutions. The engineer will contribute to testing and evaluation, reliability and observability, cloud deployment, production support, and collaboration with technical, control, and business stakeholders.

Requirements

Approximately 8 years of professional software engineering experience, including substantial hands-on Python development.
Strong understanding of software architecture, design patterns, SOLID principles, concurrency, asynchronous programming, API design, and distributed systems.
Experience with FastAPI or an equivalent backend framework, Pydantic, SQL, Git, pytest, and automated code-quality practices.
Demonstrated ability to design, implement, test, deploy, and troubleshoot production services.
Hands-on experience delivering LLM-powered applications beyond proof-of-concept demonstrations.
Strong practical knowledge of LangGraph and LangChain or equivalent frameworks, with direct LangGraph experience strongly preferred.
Experience with stateful workflows, tool calling, conditional execution, structured outputs, retries, failure recovery, and human approval patterns.
Strong knowledge of end-to-end RAG, embeddings, vector and hybrid search, reranking, grounding, and retrieval evaluation.
Experience designing repeatable LLM evaluation and regression-testing strategies.
Ability to diagnose hallucinations, retrieval failures, prompt-injection risks, tool misuse, and inconsistent agent behavior.
Hands-on experience with Azure OpenAI.
Experience with Azure identity and security services, including managed identities and Key Vault.
Experience with Docker, CI/CD, cloud deployments, automated testing, monitoring, and production troubleshooting.
Experience building secure APIs and integrating enterprise systems.
Strong understanding of authentication, authorization, least privilege, data protection, and auditability.
Bachelor's or master's degree in Computer Science, Software Engineering, AI, or a related field, or equivalent practical experience.

Nice To Haves

Direct LangGraph experience.
Experience with Azure AI Search.
Production experience implementing MCP servers and secure enterprise tools.
Experience with Azure Container Apps and Azure Kubernetes Service.
Experience with PostgreSQL or Azure Database for PostgreSQL, persistent workflow state, and object storage.
Experience with enterprise document processing and financial data extraction.
Experience with OpenTelemetry, distributed tracing, AI observability, latency optimization, and token-cost management.
Banking or other regulated-industry experience, especially credit, lending, fraud, or risk workflows.
Experience with model governance, explainability, human oversight, and responsible AI practices.

Responsibilities

Design and implement production-grade AI applications and backend services using Python, FastAPI, and modern software engineering practices.
Lead the technical implementation of stateful, multi-step agentic workflows using LangGraph and LangChain v1.
Implement graph state management, conditional routing, checkpointing, retries, recovery, idempotency, and controlled execution.
Build enterprise RAG architectures involving ingestion, chunking, embeddings, Azure AI Search, hybrid retrieval, reranking, metadata filtering, and source-grounded generation.
Integrate Azure OpenAI, enterprise APIs, document repositories, databases, and internal systems using secure API contracts.
Design and implement MCP servers and tools with authentication, authorization, input validation, and appropriate access controls.
Build reusable application components, API interfaces, shared libraries, and common AI engineering patterns.
Implement human-in-the-loop approval gates, workflow state persistence, audit trails, and safeguards against unauthorized downstream actions.
Establish automated quality gates for unit and integration testing, retrieval evaluation, agent behavior, security, and regression testing.
Engineer for reliability, scalability, latency, cost, observability, and maintainability.
Implement secure cloud application patterns using Azure identity, managed identities, Key Vault, network controls, and appropriate secrets management.
Containerize and deploy services through CI/CD pipelines to Azure Container Apps, using Azure Kubernetes Service where workload requirements justify it.
Integrate Azure Monitor and application telemetry for logs, metrics, tracing, incident investigation, and operational support.
Review code, guide implementation decisions, troubleshoot complex production defects, and mentor engineers while remaining an active developer.
Collaborate with AI leadership, application teams, architecture, cybersecurity, model risk, legal, and business stakeholders to deliver governed banking AI solutions.