AI Manager - Neuro-Symbolic

Role Overview

We are seeking an accomplished Neuro-Symbolic AI Manager to lead advanced AI research, architecture, and product development initiatives focused on hybrid intelligent systems that combine machine learning, deep learning, symbolic reasoning, and knowledge representation.

The ideal candidate will have a strong research background combined with proven experience taking advanced AI concepts from experimentation and algorithm development through scalable enterprise production deployment. This role requires both deep technical expertise and leadership experience managing multidisciplinary AI research and engineering teams.

Key Responsibilities

Define and drive the technical roadmap for Neuro-Symbolic AI, reasoning systems, and hybrid AI architectures.
Lead AI initiatives from research, experimentation, and algorithm design through enterprise-scale production deployment.
Design and optimize neuro-symbolic AI models for complex, large-scale real-world applications.
Develop hybrid AI architectures integrating deep learning, symbolic reasoning, knowledge graphs, knowledge representation, and probabilistic logic.
Lead and mentor AI researchers, machine learning engineers, and data scientists.
Collaborate with research organizations, academic institutions, engineering teams, and technology partners.
Drive innovation through advanced AI research, intellectual property, patents, and technical publications.
Ensure AI platforms and solutions are scalable, explainable, secure, reliable, and production-ready.
Architect scalable AI services and microservices for enterprise deployment.

Required Qualifications

15+ years of experience in Artificial Intelligence, Machine Learning, AI research, software engineering, or applied AI.
PhD in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a closely related field.
Advanced programming expertise in Python and Go.
Strong hands-on experience with PyTorch, PyTorch Geometric (PyG), and PyKEEN.
Deep expertise in Neuro-Symbolic AI, symbolic reasoning, knowledge representation, and hybrid AI systems.
Strong experience with Graph Neural Networks (GNNs), Knowledge Graphs, probabilistic logic, and automated reasoning.
Experience developing Explainable AI (XAI) and interpretable AI systems.
Hands-on experience deploying AI/ML platforms and applications on AWS or Azure.
Experience designing scalable microservices architectures for AI/ML platforms.
Demonstrated leadership experience managing and mentoring multidisciplinary AI/ML research and engineering teams.

Preferred Experience

Background working within advanced AI research organizations, technology companies, or AI innovation labs.
Experience applying advanced AI within knowledge-intensive industries such as healthcare, financial services, retail, robotics, autonomous systems, or industrial environments.
Experience with Reinforcement Learning, Multimodal AI, Large Language Models (LLMs), AI Agents, and Knowledge Graphs.
Proven experience transitioning AI research prototypes into enterprise-scale production systems.