Job Description
Are you ready to build the intelligence of tomorrow?
Quantum Leap Dynamics is pioneering the next generation of autonomous systems. We are seeking a visionary 2026 Visionary AI Architect to lead the design and implementation of cutting-edge agentic workflows that will redefine human-machine interaction.
In this role, you will bridge the gap between theoretical artificial general intelligence concepts and practical, scalable engineering solutions. You will architect the core systems that empower our agents to reason, plan, and execute complex tasks autonomously.
Why join us?
- Work on a roadmap that defines the future of AI for the year 2026 and beyond.
- Competitive compensation and equity packages for top-tier talent.
- Access to state-of-the-art infrastructure and proprietary datasets.
- Flexible remote-first culture with opportunities for in-person collaboration.
Responsibilities
- Architect Multi-Agent Systems: Design and implement scalable, distributed architectures for autonomous AI agents capable of complex, multi-step reasoning and tool usage.
- Develop Agentic Frameworks: Build the foundational frameworks that enable agents to plan, learn, and adapt in dynamic environments.
- Optimize Inference Pipelines: Engineer high-performance systems to reduce latency and maximize throughput for real-time AI decision-making.
- Drive 2026 Roadmap: Translate high-level future technologies into concrete technical specifications and engineering milestones.
- Collaborate with Interdisciplinary Teams: Partner with product managers, data scientists, and security experts to ensure robust and ethical AI deployment.
- Define Standards: Establish best practices, coding standards, and architectural patterns for the AI engineering organization.
Qualifications
- Advanced Degree: Ph.D. or Master’s degree in Computer Science, Mathematics, or a related field, with a focus on AI/ML.
- Technical Expertise: Extensive experience with Python, C++, and modern deep learning frameworks (PyTorch, TensorFlow).
- AI Proficiency: Deep understanding of Large Language Models (LLMs), Reinforcement Learning from Human Feedback (RLHF), and Retrieval-Augmented Generation (RAG).
- System Design: Proven track record of designing complex, fault-tolerant distributed systems and microservices.
- Agentic Knowledge: Experience with agent orchestration tools (LangChain, AutoGen) and memory architectures.
- Innovation Mindset: Demonstrated ability to innovate and stay ahead of rapidly evolving AI trends.