What are the responsibilities and job description for the AI Solutions Engineer position at Infused Solutions?
Location: New York, NY (3 days on-site)
Compensation: $160K-$200K base 20 – 30% bonus equity
Job Type: Permanent
Infused Solutions’ client is a fast-growing AI platform serving institutional finance clients. They automate complex analyst workflows-turning days of manual work into minutes-for some of the world’s largest financial institutions.
The Role: AI Solutions Engineer
30% financial analyst (understand workflows intimately) 30% product strategist (translate needs into solutions) 40% hands-on builder (prototype and ship working AI agents)
You will:
Compensation: $160K-$200K base 20 – 30% bonus equity
Job Type: Permanent
Infused Solutions’ client is a fast-growing AI platform serving institutional finance clients. They automate complex analyst workflows-turning days of manual work into minutes-for some of the world’s largest financial institutions.
The Role: AI Solutions Engineer
30% financial analyst (understand workflows intimately) 30% product strategist (translate needs into solutions) 40% hands-on builder (prototype and ship working AI agents)
You will:
- Shadow credit analysts at PE firms and asset managers, reverse-engineer their workflows (IC memos, deal screening, portfolio monitoring, credit risk)
- Design multi-step AI agent logic that automates those workflows
- Prototype and ship V1s in days, present to partners/C-suite
- Manage scope, drive adoption, hand prototypes to engineers for productization
- Build production infrastructure (that’s Product Engineers)
- Manage a team (you’re IC, hands-on)
- Sit in an office writing requirements doc (you’re embedded with clients)
- Institutional finance analyst background (4-7 years) IB (credit, DCM, leveraged finance), PE/credit, or asset management. You know what an IC memo looks like and have built financial models at 2 AM.
- Self-taught coder Python, SQL, basic scripting. You can prototype and automate your own workflows (Excel → Python → Lang Chain).
- Built something with LLMs in the last 12 months RAG pipeline, agentic workflow, document automation-anything. You’ve shipped something real (internal tool, side project, client prototype).
- Bridge analyst and CTO Translate business problems into technical requirements. Explain AI limitations to skeptical partners. Push back on scope creep.
- Ship fast V1 in days, not months. You’re comfortable with “good enough to prove value” and iterate based on feedback.