Job Description
We are on a mission to define the future of intelligence. Nexus Horizon Labs is seeking a visionary Lead AI Architect to spearhead the development of our 2026 technology stack. In this pivotal role, you will not just build models; you will architect the ecosystems that will power the next decade of human-machine collaboration.
As the industry shifts towards Autonomous Agents and Multimodal AI, we need a leader who understands both the theoretical depth and the engineering rigor required to bring these concepts to production. You will work directly with our CTO and a world-class team of data scientists and engineers to build scalable, ethical, and high-performance AI solutions.
Why join us?
We offer competitive equity, remote-first flexibility, and the opportunity to shape the industry standards for the year 2026 and beyond.
Responsibilities
- Architect the Future: Design and implement the core infrastructure for next-generation Generative AI and Agentic workflows.
- Model Optimization: Lead research into model distillation, quantization, and efficient inference to reduce latency and cost.
- System Scalability: Build fault-tolerant, distributed machine learning pipelines capable of handling petabyte-scale data.
- Technical Leadership: Mentor senior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Ethical AI Governance: Establish guidelines and frameworks to ensure AI safety, bias mitigation, and compliance with emerging regulations.
- Cross-Functional Collaboration: Partner with product and engineering teams to translate complex AI capabilities into user-centric products.
Qualifications
- Experience: 8+ years of software engineering experience, with at least 5 years in AI/ML architecture and leadership.
- Tech Stack: Deep expertise in Python, PyTorch, or TensorFlow. Experience with LangChain, LlamaIndex, or similar orchestration frameworks.
- Cloud Mastery: Proven track record deploying AI models on AWS, GCP, or Azure using Kubernetes and containerization.
- Education: MS or PhD in Computer Science, Machine Learning, or a related quantitative field is highly preferred.
- Problem Solving: Demonstrated ability to solve complex, unstructured problems in high-stakes environments.
- Communication: Exceptional ability to communicate technical concepts to non-technical stakeholders.