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NEXTMOVEFDE careers · United States
Chatham Financial

AI Solutions Engineer

AI summary of the role

Chatham Financial seeks an AI Solutions Engineer to design, build, and deploy custom AI solutions for high-value business workflows, acting as a hybrid technologist and trusted advisor.

What you’ll do

  • Work directly with private equity firms, real estate investors, and capital markets clients to understand workflows and identify AI opportunities
  • Architect custom AI solutions including workflow automation, data extraction, intelligent reporting, and AI agents
  • Write production code in Python and integrate LLMs (OpenAI, Anthropic) to build RAG systems and agentic workflows
  • Build working prototypes in days/weeks and iterate based on real user feedback

What you’ll bring

  • 7+ years of software engineering experience, ideally with client-facing or consulting exposure
  • Strong programming skills in Python
  • Hands-on experience building with LLMs (GPT, Claude) - RAG, prompt engineering, fine-tuning, or agentic systems
  • Experience taking AI/ML projects from prototype to production

Technologies

Python · TypeScript · JavaScript · LLM · GPT · Claude · RAG · OpenAI · Anthropic · AWS · Azure · GCP

Source and classification

Production engineering · Evidence for this classification:

You'll own problems end-to-end—from discovery through production. This is a hybrid technologist and trusted advisor role. You'll embed with client teams, understand their operational challenges, and build AI-powered solutions that create measurable value. Think startup CTO meets management consultant—you'll own problems end-to-end. What You'll Do: Client Engagement Work directly with private equity firms, real estate investors, and capital markets clients to understand their workflows and identify AI opportunities Lead discovery conversations to uncover pain points and scope solutions Present recommendations to both technical and executive stakeholders Solution Design & Architecture Architect custom AI solutions including workflow automation, data extraction, intelligent reporting, and AI agents Evaluate when to use LLMs vs. traditional ML vs. rule-based approaches Design
How jobs are selected

Employer postings · Data from · Sources