Job Description
The Future of Intelligence is Here.
Quantum Leap Technologies is pioneering the next generation of Artificial Intelligence systems. We are looking for a visionary Senior AI Architect (2026 Vision) to design and deploy scalable, cutting-edge machine learning infrastructure that will define the technological landscape of the coming decade.
In this role, you will not just write code; you will architect the neural pathways of tomorrow. You will lead a team of elite engineers to build models capable of real-time reasoning, autonomous decision-making, and seamless integration into enterprise ecosystems.
Why Join Us?
- Work on projects that are shaping the roadmap for 2026 and beyond.
- Competitive equity package and top-tier healthcare.
- Flexible remote-first culture with a hub in Austin, TX.
Responsibilities
- Architectural Vision: Design robust, scalable AI system architectures that handle billions of data points with zero latency.
- Model Development: Lead the research and implementation of state-of-the-art Deep Learning and Generative AI models.
- Strategic Roadmapping: Define the technical strategy for 2026, identifying emerging technologies (e.g., Quantum Computing interfaces, AGI readiness) to integrate into our core stack.
- Team Leadership: Mentor senior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Performance Optimization: Optimize inference pipelines and model weights to ensure maximum efficiency on edge devices and cloud infrastructure.
- Collaboration: Partner with product and business units to translate complex AI capabilities into user-friendly applications.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field with a focus on AI/ML.
- Experience: 7+ years of experience in software engineering, with at least 4 years focused on AI/ML architecture.
- Technical Stack: Expert proficiency in Python, PyTorch, TensorFlow, and C++.
- Frameworks: Deep experience with Large Language Models (LLMs), Transformers, and vector databases (e.g., Pinecone, Milvus).
- Cloud Mastery: Proven track record of deploying scalable ML workloads on AWS, GCP, or Azure.
- Problem Solving: Ability to tackle complex mathematical problems and design systems that are fault-tolerant and secure.