Job Description
We are seeking a visionary Lead AI Architect to spearhead our Project 2026 initiative, a groundbreaking mission to redefine the future of generative AI infrastructure. Nexus Horizon Systems is at the forefront of the next technological evolution, and we need a technical mastermind to build the scalable, secure, and efficient systems that will power the AI landscape of tomorrow.
In this role, you will bridge the gap between theoretical AI research and production-grade engineering. You will lead a team of elite engineers, establish architectural standards for our next-gen LLMs, and ensure our systems are future-proofed for the rapid advancements expected by 2026 and beyond.
Why Join Us?
- Work on the bleeding edge of Artificial General Intelligence (AGI) research.
- Competitive equity package and performance bonuses.
- Flexible remote-first culture with state-of-the-art office amenities.
- Focus on high-impact, mission-critical projects with global reach.
Responsibilities
- Design and implement the core infrastructure for Project 2026, focusing on high-throughput, low-latency AI inference and training pipelines.
- Lead a cross-functional team of ML engineers, data scientists, and DevOps specialists to drive innovation and delivery.
- Establish and enforce best practices for MLOps, including model versioning, monitoring, and automated retraining workflows.
- Research and integrate cutting-edge algorithms and hardware accelerators (e.g., TPUs, GPUs) to optimize model performance.
- Collaborate with product leadership to translate complex AI capabilities into user-centric features.
- Ensure system scalability, security, and compliance with global data privacy regulations.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, or a related technical field.
- Minimum of 8+ years of experience in software engineering, with at least 4 years specifically in AI/ML architecture.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Proven experience designing distributed systems for machine learning workloads.
- Strong understanding of Large Language Models (LLMs), transformers, and prompt engineering.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Excellent communication skills with the ability to articulate complex technical concepts to non-technical stakeholders.