Chief Information Technology Officer

Chief Technology Officer (CTO) Valuence-AI

Location: Hybrid / Remote (U.S. REQUIRED)

THIS IS NOT A C2C OPPORTUNITY

Reports to: Founder & CEO

About Valuence-AI

Valuence-AI is building the first

Explainable Valuation Intelligence (EVI™)

platform for institutional decision-makers. We combine structured financial logic, regulatory-ready reasoning, and adaptive machine learning to produce defensible, reviewable valuations — not black-box outputs.

Our system doesn’t provide false confidence. It explicitly communicates what it knows, what it doesn’t know, and how it reached its conclusions. Every valuation includes assumptions, logic chains, and traceable reasoning — making mistakes correctable and governance-ready.

We are integrating directly into Loan Origination Systems (LOS) and enterprise workflows to become core infrastructure for valuation governance.

We are now hiring a

foundational CTO

to architect and scale the next generation of neuro-symbolic valuation intelligence.

The Role

This is not a maintenance CTO role. This is an architecture role.

You will design and lead the development of a

neuro-symbolic AI platform

that combines:

Neural networks for pattern recognition and probabilistic inference

Symbolic reasoning engines for structured financial logic

Constraint-based systems for regulatory compliance

Explainability frameworks that make reasoning auditable

Your mandate: Build AI that institutions can defend in court, in audit, and in front of regulators.

Core Responsibilities 1. Architect Neuro-Symbolic Intelligence

Design hybrid AI systems combining neural networks with symbolic reasoning engines.

Implement rule-based financial logic integrated with probabilistic ML outputs.

Build constraint-aware inference systems that respect regulatory boundaries.

Develop structured reasoning graphs for explainable outputs.

2. Build the EVI™ Core Engine

Create assumption-aware valuation pipelines.

Ensure outputs include traceable reasoning paths.

Develop uncertainty quantification frameworks.

Embed “known vs unknown” confidence signaling.

3. Lead Engineering & AI Strategy

Recruit and manage high-caliber ML and platform engineers.

Define system architecture for scale, security, and enterprise integration.

Oversee data infrastructure and model governance.

Establish AI audit and compliance protocols.

4. LOS & Enterprise Integration

Architect API-first infrastructure.

Ensure seamless integration into enterprise Loan Origination Systems.

Design secure, scalable deployment models (cloud + on-prem options).

Implement SOC2-aligned security standards.

5. Long-Term Technical Moat

Drive patentable infrastructure development.

Build proprietary reasoning pipelines.

Develop regulatory-ready explainability frameworks.

Create defensible AI architecture that cannot be easily replicated.

Required Qualifications

10+ years in software engineering, ML systems, or AI architecture.

Deep experience with:

Neural networks (PyTorch, TensorFlow, JAX, or similar)

Graph-based reasoning systems

Knowledge representation and symbolic AI

Probabilistic modeling

Experience building production-grade AI systems.

Strong understanding of uncertainty quantification and model validation.

Familiarity with explainability techniques (SHAP, LIME, causal modeling, etc.).

Experience scaling SaaS infrastructure.

Highly Preferred

Experience with neuro-symbolic AI or hybrid reasoning systems.

Background in fintech, proptech, valuation, or regulated industries.

Experience building audit-ready AI systems.

Knowledge of valuation methodologies (DCF, comparable sales, income approach, etc.).

Familiarity with governance and regulatory compliance frameworks.

What Success Looks Like (First 18 Months)

Launch Version 2 of the EVI™ neuro-symbolic engine.

Deploy LOS-integrated valuation workflows.

Establish model explainability standards for institutional adoption.

File at least 1–3 core architecture patents.

Build and lead a 5–10 person AI engineering team.

Deliver enterprise-grade scalability and security readiness.

Leadership Profile

You are:

A systems thinker, not a feature builder.

Comfortable operating in ambiguity.

Obsessed with first-principles architecture.

Fluent in both deep learning and structured logic.

Motivated by building category-defining infrastructure.

You understand that explainability is not a checkbox — it is architecture.

Compensation

Competitive early-stage salary – 10% Equity + 10% Additional upon KPI’s met= 20% Equity

Base Salary once seed funding is decured is $150k, OTE $300,000.

Base by EOY 6 $410K – OTE $600K+

Meaningful founding-level equity

Performance-based milestone upside

Opportunity to define an entirely new AI category

Why This Matters

Black-box AI cannot survive in regulated valuation environments. Institutions require systems that are:

Defensible

Reviewable

Correctable

Transparent

Valuence-AI is building the infrastructure layer for institutional valuation governance.

If you want to build AI that regulators trust, courts respect, and institutions depend on — this is your role.

#J-18808-Ljbffr