Fuse Finance - Founding AI Engineer

Company Overview Fuse is a high‑growth New York City startup revolutionizing financial services infrastructure through an innovative low‑code tool. Backed by top‑tier venture capital, the company is creating a transformative self‑service platform for configuration, integration, and automation in financial technology. We are now expanding our capabilities to leverage artificial intelligence in revolutionizing how financial services workflows and integrations are built.

Role Overview We’re hiring a

Founding AI Engineer

to spearhead our

AI agent

initiatives and accelerate

AI product MVPs

to real clients. You’ll combine strong

fullstack engineering (TypeScript)

with LLM Ops to design, build, and deploy prototypes that validate product–market fit quickly—while establishing rigorous evaluation and human‑in‑the‑loop safeguards for quality and trust. You’ll collaborate with Product Operations, coordinate two junior/mid engineers, and work with Design to deliver end‑to‑end—from ideation to production.

Core Focus (What Matters Most)

Rapid AI Prototyping & MVP Delivery : Design, build, and ship MVPs to clients to validate hypotheses and speed time‑to‑market.

Pragmatic Full‑Stack + Low‑Code : Enhance our low‑code platform by generating/optimizing

workflows, UIs, and data schemas

powered by AI agents.

Performance Metrics & Human‑in‑the‑Loop : Define and monitor accuracy, confidence, and error rates; create

evaluation frameworks

and set confidence thresholds that trigger human review.

End‑to‑End Ownership : Act as the MVP’s “CEO/CTO”—align with client needs, deploy, learn from real usage, and iterate aggressively.

Cross‑Functional Leadership : Partner with Product/Engineering and Design to integrate AI seamlessly and hit aggressive timelines.

Responsibilities

Build and optimize AI agent pipelines leveraging leading LLMs and AI services to extend our low‑code platform.

Develop

agentic workflows

that ensure high‑quality answers with strong domain grounding.

Generate and validate use‑case‑specific workflows, UIs, and data schemas.

Create

evaluation frameworks

to measure and improve output quality.

Integrate AI capabilities into the existing platform architecture and establish monitoring, versioning, and continuous improvement practices.

Must‑Have Qualifications

8+ years

in fullstack engineering with proven, hands‑on

AI agent workflow

implementation.

Expertise in

TypeScript/Node.js ,

LLM integration

(GPT, Claude, Gemini, Grok), and

prompt engineering .

Experience with

agent evaluation ,

pipeline design , and

prompt/version control .

Strong foundations in

RESTful APIs ,

data modeling ,

architectural patterns , and

authN/authZ

for AI environments.

Track record of

production‑grade AI systems

delivering measurable business value.

Technical Expertise

LLMs & LLMOps ; agent evaluation/optimization with tools like

Deepchecks ,

OpenAI Evals ,

LangSmith

(or similar).

AWS ,

SQL/optimization ,

ETL ,

RESTful API

integration/management/design, and

software development best practices .

Ideal Project Experience

Fine‑tuning LLMs;

agent‑centered workflow pipelines ; generative AI applications; automated testing/validation; documentation and knowledge sharing.

Technical Environment

Languages : TypeScript / Node.js, Low‑code tools (Loveable, n8n)

AI/ML Frameworks : e.g.,

LangChain ,

LlamaIndex ,

Haystack ,

Flowise

Cloud :

AWS

Database :

PostgreSQL

Version Control :

Git

CI/CD :

GitHub Actions

Nice to Have

Python

(PyTorch, TensorFlow, Pandas or similar).

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