Job Description
Are you ready to architect the digital infrastructure for the year 2026 and beyond? At Nexus Horizon AI, we are building the autonomous systems that will define the next decade of human-machine interaction. We are looking for a visionary Future-Ready AI Systems Architect to lead the design, deployment, and scaling of our next-generation Agentic AI platforms.
In this role, you won't just maintain legacy systems; you will build the foundational architecture for tomorrow's autonomous intelligence. You will work at the intersection of Deep Learning, Distributed Systems, and Generative AI to create scalable solutions that anticipate the market needs of 2026.
Why Join Us?
- Work on cutting-edge Agentic Workflows and LLM Orchestration.
- Competitive equity package and remote-first culture.
- Access to the latest GPU clusters and research hardware.
Responsibilities
- Architect Scalable AI Infrastructures: Design and implement robust distributed systems capable of handling millions of concurrent agent interactions for 2026 scalability targets.
- Orchestrate Agentic Workflows: Develop complex logic for autonomous agents, ensuring seamless integration between Large Language Models (LLMs) and external data sources.
- Optimize Model Performance: Engineer high-throughput, low-latency inference pipelines using cutting-edge quantization and caching strategies.
- Future-Proof Security: Implement Zero-Trust architectures and advanced encryption protocols to secure proprietary AI training data.
- Research & Prototyping: Collaborate with R&D teams to prototype novel AI paradigms and evaluate emerging technologies for immediate implementation.
- System Monitoring: Build real-time observability dashboards to proactively detect and resolve bottlenecks in autonomous agent lifecycles.
Qualifications
- Advanced Technical Background: 5+ years of experience in Software Engineering, with at least 3 years specifically in AI/ML infrastructure or Distributed Systems.
- Language Proficiency: Deep expertise in Python, C++, or Rust; familiarity with PyTorch and TensorFlow.
- AI Specialization: Proven track record of deploying production-grade LLMs (e.g., Llama, GPT-4, Claude) and experience with RAG (Retrieval-Augmented Generation) architectures.
- Cloud Architecture: Strong experience with cloud providers (AWS, GCP, or Azure) and container orchestration (Kubernetes/Docker).
- Problem Solving: Ability to architect solutions for ambiguous, future-facing problems with a focus on modularity and extensibility.
- Education: Bachelor’s degree in Computer Science, Engineering, or a related field (Master’s degree preferred).