WF - Finance Analyst 4 - 233782

job summary:



AI Engineer - Innovation & Agentic AI

• Location: Charlotte, NC (On-site 3 days/week)
• Pay Rate: $65.00 / hr (W-2)
• Term: 24-Month Contract
• Interview Process: 1-round via MS Teams (45 mins)

Overview Seeking a hands-on AI Engineer to join the Innovation & AI team to rapidly prototype next-generation AI capabilities for Treasury. This role focuses on translating business opportunities into working solutions, building intelligent agents that reason across enterprise context layers, and establishing reusable patterns for enterprise adoption.

Key Responsibilities

• Rapidly design, build, and deploy AI prototypes, intelligent agents, and context-aware copilots for Treasury use cases.
• Develop agentic workflows that plan, reason, call tools, maintain state, and handle multi-step reasoning using Python and TypeScript.
• Integrate LLM applications with a DataHub-based graph context layer, enterprise APIs, SQL databases, and analytical tools.
• Evaluate models, agent frameworks, context-engineering techniques, and reasoning quality based on latency, reliability, cost, and security.

Qualifications

• Core Engineering: Strong hands-on experience in Python and TypeScript (APIs, services, testing, debugging, GitHub collaboration).
• AI/Agentic Frameworks: Proven experience building generative/agentic AI solutions using LangGraph , LangChain , or similar orchestration frameworks; hands-on tool calling and state management experience.
• Platforms & Tools: Experience with Azure OpenAI (or enterprise equivalent), REST APIs, SQL, and structured/unstructured data.
• Nice-to-Have: DataHub / Knowledge Graph exposure, React, multi-agent architecture, agent observability/tracing, or Financial Services/Treasury domain knowledge.

location: Charlotte, North Carolina
job type: Contract
work hours: 9 to 5
education: Unknown
experience: 5 Years

responsibilities:



Key Responsibilities

• Rapidly design, build, and deploy AI prototypes, intelligent agents, and context-aware copilots for Treasury use cases.
• Develop agentic workflows that plan, reason, call tools, maintain state, and handle multi-step reasoning using Python and TypeScript.
• Integrate LLM applications with a DataHub-based graph context layer, enterprise APIs, SQL databases, and analytical tools.
• Evaluate models, agent frameworks, context-engineering techniques, and reasoning quality based on latency, reliability, cost, and security.

 
qualifications:



Qualifications

• Core Engineering: Strong hands-on experience in Python and TypeScript (APIs, services, testing, debugging, GitHub collaboration).
• AI/Agentic Frameworks: Proven experience building generative/agentic AI solutions using LangGraph , LangChain , or similar orchestration frameworks; hands-on tool calling and state management experience.
• Platforms & Tools: Experience with Azure OpenAI (or enterprise equivalent), REST APIs, SQL, and structured/unstructured data.
• Nice-to-Have: DataHub / Knowledge Graph exposure, React, multi-agent architecture, agent observability/tracing, or Financial Services/Treasury domain knowledge.

 
skills: Treasury




Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.

At Randstad, we welcome people of all abilities and want to ensure that our hiring and interview process meets the needs of all applicants. If you require a reasonable accommodation to make your application or interview experience a great one, please contact View email address on us.fitly.work.




Pay offered to a successful candidate will be based on several factors including the candidate's education, work experience, work location, specific job duties, certifications, etc. In addition, Randstad offers a comprehensive benefits package, including health, an incentive and recognition program, and 401K contribution (all benefits are based on eligibility).




This posting is open for thirty (30) days.