Job Description
We are seeking a visionary Senior AI Architect to spearhead our 2026 strategic roadmap. At Nexus Horizon Systems, we are building the intelligence layer for the next generation of enterprise solutions. You will define the architectural blueprints for our next-gen generative models, ensuring scalability, ethical AI compliance, and groundbreaking performance.
In this role, you will bridge the gap between theoretical AI research and production-grade engineering, leading a team of top-tier data scientists and ML engineers. If you are passionate about shaping the future of technology and want to leave a lasting impact on the industry, we want to hear from you.
Why Join Us?
- Future-Proof Your Career: Work on projects that are defining the 2026 technological landscape.
- Top-Tier Compensation: Competitive salary, equity package, and comprehensive benefits.
- Remote-First Culture: Flexible work arrangements with a global team of innovators.
Responsibilities
- Design and implement the core architecture for next-generation Large Language Models (LLMs) and multimodal systems.
- Lead the end-to-end machine learning pipeline, from data ingestion to model deployment and monitoring.
- Collaborate with product leadership to translate business goals into technical AI strategies.
- Ensure system scalability, latency optimization, and fault tolerance for high-traffic applications.
- Establish best practices for AI ethics, data privacy, and model explainability.
- Conduct code reviews, mentor junior architects, and foster a culture of continuous learning.
- Stay ahead of industry trends, specifically regarding advancements in Quantum AI and Neural Processing Units (NPUs).
Qualifications
- Master’s degree or PhD in Computer Science, Mathematics, or a related technical field (or equivalent experience).
- 8+ years of experience in software engineering, with at least 4 years specifically in AI/ML architecture.
- Deep expertise in Python, TensorFlow, PyTorch, and Hugging Face ecosystem.
- Proven track record of deploying production-level ML models serving millions of requests.
- Strong understanding of distributed systems, cloud architecture (AWS/GCP/Azure), and containerization (Docker/Kubernetes).
- Experience with MLOps tools (MLflow, Kubeflow) and A/B testing frameworks.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.