Job Description
We are not just building software for tomorrow; we are architecting the operating system for the year 2026. Nexus Horizon Systems is at the forefront of the Agentic AI revolution, creating autonomous agents that redefine human-computer interaction. We are seeking a visionary 2026 Agentic AI Architect to lead the design and deployment of next-generation autonomous intelligence systems.
In this pivotal role, you will bridge the gap between theoretical AI research and production-grade engineering. You will be responsible for building the infrastructure that allows autonomous agents to perceive, reason, and act independently in complex environments. If you are passionate about the future of AI and want to shape the landscape of 2026, we want to hear from you.
Why Join Us?
- Work on the cutting edge of Agentic AI and Autonomous Systems.
- Competitive compensation and equity packages.
- Flexible remote-first culture with a focus on output over hours.
- Access to state-of-the-art compute resources and research papers.
Responsibilities
- Design and implement scalable architectures for autonomous AI agents capable of complex, multi-step reasoning and execution.
- Lead the integration of Large Language Models (LLMs) with external tool use, APIs, and real-time data streams.
- Optimize model inference latency and throughput for edge and cloud deployment environments.
- Establish robust evaluation frameworks and metrics to measure agent reliability, safety, and performance.
- Collaborate with cross-functional teams of researchers, product managers, and security experts to define the roadmap for 2026 capabilities.
- Mentor junior engineers and establish coding standards for AI infrastructure projects.
Qualifications
- PhD or Master's degree in Computer Science, Artificial Intelligence, or a related technical field (or equivalent professional experience).
- 5+ years of experience in software engineering, with at least 3 years focused on Machine Learning, NLP, or Reinforcement Learning.
- Strong proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow.
- Deep understanding of LLM architecture, prompt engineering, and fine-tuning methodologies (LoRA, QLoRA).
- Experience designing distributed systems and microservices for high-scale AI applications.
- Proven track record of shipping production-grade AI products or open-source contributions.
- Familiarity with AI safety, alignment research, and responsible AI practices.