Senior Applied AI Engineer​/Financial Services​/Miami

A growing fintech company focused on modern financial services and trading technology is looking for a Senior Applied AI Engineer to build secure, production-grade AI applications supporting financial services workflows. The company combines financial services expertise with advanced data, AI, and engineering capabilities, with a strong focus on security, auditability, correctness, and reliability. The engineering environment is primarily built around the Microsoft Azure ecosystem and integrates closely with .NET-based

enterprise systems and sensitive financial data.

As a Senior Applied AI Engineer, you'll focus on turning emerging AI capabilities into reliable applications that can be deployed and used in real-world financial workflows. You'll build AI-powered applications using C#, .NET, and Azure while designing and implementing RAG pipelines, agentic workflows, and LLM integrations. This is a hands-on engineering position focused on integrating AI into production systems through APIs, function calling, tool orchestration, and enterprise system integrations rather than model training or fundamental AI research.

You'll work across the application and AI stack to develop backend services and APIs that expose AI capabilities while implementing the guardrails, validation, observability, and human-in-the-loop workflows required for production financial applications. Working closely with Product, Data, Platform, and AI teams, you'll help transform AI prototypes into secure, scalable, and dependable products that can operate within enterprise and regulated environments.

The ideal candidate is a strong software engineer who has developed production applications and has hands-on experience applying generative AI, LLMs, RAG, and agent workflows to real-world problems. You'll have the opportunity to work with technologies including Azure OpenAI, Azure AI Search, AKS, Azure Functions, Service Bus, Cosmos DB, and modern AI orchestration frameworks while helping define how AI can be integrated into critical financial services workflows.

• 5+ years of professional software engineering experience

• Strong experience with C# / .NET and Microsoft Azure

• Experience building production APIs and backend services

• Hands-on experience integrating Generative AI and Large Language Models into production applications

• Experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines

• Experience building agent workflows and intelligent workflow applications

• Experience with prompt engineering

• Experience with function calling and AI tool orchestration

• Experience working with SQL, enterprise systems, and cloud-native architectures

• Experience building AI assistants, copilots, or similar intelligent applications

• Ability to take AI prototypes and turn them into reliable, production-grade applications

• Experience working within financial services or another regulated industry

• Experience with .NET 8+, ASP.NET Core, and modern C# development

• Experience with Azure OpenAI and Azure AI Search

• Experience with Azure Kubernetes Service (AKS)

• Experience with Azure Functions and Service Bus

• Experience with Cosmos DB and SQL

• Experience with Docker and containerized applications

• Experience with Git Hub Actions or Azure Dev Ops

• Familiarity with Semantic Kernel, Lang Chain, Copilot Studio, Azure AI Foundry, or similar AI orchestration frameworks

• Experience with React, Type Script, Blazor, or Python

• Experience developing secure AI applications in enterprise environments

• Strong understanding of cloud-native application architectures

• Build AI-powered applications using C#, .NET, and Azure

• Design and implement RAG pipelines that connect LLMs with enterprise data and systems

• Develop agentic workflows that enable AI applications to reason, interact with tools, and execute business processes

• Integrate LLMs into production applications using APIs, function calling, and tool orchestration

• Develop backend services and APIs that expose AI capabilities to internal applications and workflows

• Implement guardrails, validation logic, observability, and human-in-the-loop workflows

• Build AI assistants, copilots, and intelligent workflow applications for financial services use cases

• Integrate AI capabilities with enterprise systems, databases, and cloud-native services

• Work closely with Product, Data, Platform, and AI teams to define and deliver production solutions

• Evaluate AI prototypes…