AI Engineer – Agentic AI & Innovation
Location: Charlotte, NC
Work Model: Hybrid – Minimum 3 Days Onsite Per Week
About the Opportunity
A leading financial services organization is seeking a hands-on AI Engineer to join an Innovation & AI team focused on building and advancing next-generation Generative AI and Agentic AI solutions.
This is a highly technical, hands-on role for an engineer who enjoys taking emerging business concepts and rapidly turning them into working prototypes. You will design intelligent agents, AI-enabled applications, and multi-step workflows that interact with enterprise data, APIs, metadata, applications, and analytical tools.
The ideal candidate brings strong Python and TypeScript development experience, hands-on expertise with modern AI agent frameworks such as LangGraph and LangChain, and experience collaborating within modern Git/GitHub-based development environments.
What You'll Do
- Design and build Generative AI and Agentic AI prototypes, proofs of concept, and technical demonstrations
- Develop intelligent agents, enterprise copilots, and AI-enabled decision-support applications
- Build agentic workflows incorporating tool calling, orchestration, state management, context engineering, structured outputs, and multi-step reasoning
- Develop Python services, APIs, tools, automation, and reusable AI components
- Build lightweight full-stack experiences to test new AI interaction models
- Integrate AI applications with enterprise data, metadata, APIs, applications, and analytical platforms
- Enable AI agents to reason across relationships between data assets, systems, business processes, models, controls, and business concepts
- Evaluate LLMs, agent frameworks, orchestration approaches, and context-engineering techniques
- Assess solutions based on reliability, reasoning quality, latency, cost, security, and business value
- Work collaboratively within shared codebases using Git/GitHub and modern software development practices
- Document reusable engineering patterns, technical constraints, lessons learned, and recommendations
- Partner with business, architecture, data, cybersecurity, risk, and technology teams to help move successful prototypes toward enterprise adoption
Required Qualifications
- Hands-on experience building Generative AI, Agentic AI, machine learning, or advanced software solutions
- Strong development experience with Python and TypeScript
- Experience building applications or intelligent agents powered by large language models (LLMs)
- Hands-on experience with LangGraph, LangChain, or comparable agent orchestration frameworks
- Experience with tool calling, agent orchestration, state management, context engineering, structured outputs, and multi-step AI workflows
- Experience with Azure OpenAI or another enterprise AI platform
- Strong experience developing and integrating REST APIs and services
- Experience integrating AI applications with enterprise data, applications, APIs, metadata, or analytical tools
- Knowledge of SQL and structured/unstructured data
- Working knowledge of graph-based data structures, knowledge graphs, or metadata-driven applications
- Strong Git/GitHub experience and familiarity working collaboratively within shared codebases
- Ability to independently take an ambiguous or emerging business concept from idea to functioning prototype
- Ability to build modular, documented, testable, and maintainable solutions
- Strong communication skills with the ability to explain technical decisions, risks, limitations, and tradeoffs
Nice to Have
- DataHub, enterprise metadata platforms, knowledge graphs, graph databases, or semantic data layers
- Multi-agent systems, enterprise copilots, or AI-enabled decision-support tools
- React, JavaScript, MongoDB, or additional full-stack development experience
- Agent evaluation, observability, tracing, guardrails, human-in-the-loop workflows, or AI cost monitoring
- Cloud-native development, containerization, CI/CD, and automated deployments
- Experience working across multiple foundation models and evaluating model-selection tradeoffs
- GitHub Copilot or other AI-assisted software engineering tools
- Financial services, Treasury, liquidity, funding, forecasting, risk, or regulatory experience
- Responsible AI, data governance, cybersecurity, model risk, or enterprise technology controls
What Makes This Opportunity Exciting
This role sits at the intersection of AI innovation and enterprise-scale financial technology. Rather than simply maintaining an established application, you'll have the opportunity to experiment with emerging AI capabilities, build working solutions, establish reusable engineering patterns, and help shape how intelligent agents can be deployed within a complex enterprise environment.