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Finance Analyst - Contingent
Job in
Charlotte, Mecklenburg County, North Carolina, 28282, USA
Listed on 2026-09-09
Listing for:
Pinnacle Technical Resources
Full Time
position Listed on 2026-09-09
Job specializations:
-
Software Development
AI Engineer (Applied/Software)
Job Description & How to Apply Below
Position:
Finance Analyst
Location:
Charlotte, NC (Hybrid – 3 days onsite; 2 days remote) Duration: 24 months contract Job Job Overview We are seeking a hands-on AI Engineer to join an Innovation and AI team supporting next-generation AI solutions for Treasury. This role will focus on rapidly developing AI prototypes, intelligent agents, enterprise copilots, and AI-enabled decision-support solutions. The ideal candidate will have strong experience with Generative AI, Agentic AI, Python, Type Script, LLMs, and agent orchestration frameworks.
You will work closely with business and technology teams to turn complex business problems into working AI solutions while developing reusable patterns that can eventually scale into enterprise platforms.
Key Responsibilities Design and build AI prototypes, proofs of concept, and technical demonstrations. Partner with Treasury business users to identify high-value AI opportunities. Develop intelligent agents, enterprise copilots, and AI-powered decision-support solutions. Build agentic workflows involving tool calling, planning, reasoning, state management, and human oversight. Integrate AI solutions with enterprise data, APIs, applications, and analytical tools. Work with graph-based enterprise context and metadata to help AI agents understand relationships between data, systems, processes, controls, and business concepts.
Develop Python services, APIs, automation, and reusable AI components. Build lightweight full-stack applications to test new AI experiences with business users. Evaluate AI models and agent frameworks based on reliability, accuracy, latency, cost, security, and business value. Establish success metrics and evaluate agent reasoning, contextual accuracy, tool execution, and overall user experience. Document technical designs, architectural patterns, lessons learned, and recommendations.
Collaborate with architecture, data, cybersecurity, risk, and control teams to support enterprise adoption. Develop reusable AI engineering patterns for future Treasury initiatives.
Required Qualifications Hands-on experience with Generative AI, Agentic AI, Machine Learning, or advanced software engineering. Strong Python and Type Script development experience. Experience building APIs, services, automation, testing, and debugging. Experience developing applications powered by Large Language Models (LLMs).
Experience with agent orchestration, tool calling, state management, context engineering, structured outputs, and multi-step workflows.
Experience with Lang Graph, Lang Chain, or similar agent frameworks.
Experience with Azure OpenAI or another enterprise AI platform. Experience integrating AI applications with enterprise data, APIs, metadata, applications, or analytical tools. Understanding knowledge graphs, graph-based data, metadata platforms, or semantic data layers. Strong knowledge of REST APIs, SQL, Git, structured/unstructured data, and modern software development practices. Ability to independently take an ambiguous business problem and turn it into a functional prototype.
Strong communication skills with the ability to explain technical concepts, risks, limitations, and tradeoffs to business and technology stakeholders. Nice-to-Have Skills
Experience with Datahub, enterprise metadata platforms, knowledge graphs, graph databases, or semantic data layers. Experience building multi-agent systems, enterprise copilots, or AI decision-support applications. Full-stack development experience with React, Type Script, JavaScript, or MongoDB.
Experience with AI evaluation, observability, tracing, guardrails, human oversight, and cost monitoring.
Experience with cloud-native development, containers, CI/CD, and automated deployments. Experience working with multiple foundation models and understanding model-selection tradeoffs.
Experience with Git Hub Copilot or other AI-assisted development tools. Experience in Financial Services, Treasury, Liquidity, Funding, Forecasting, Risk, or Regulatory environments. Knowledge of Responsible AI, Data Governance, Cybersecurity, Model Risk, and enterprise technology controls. What Success Looks Like Success in this…
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