Job Description
We are on the cusp of a technological renaissance. Nexus Horizon Systems is looking for a visionary Lead AI Architect to define the infrastructure for our revolutionary 2026 roadmap. If you are passionate about shaping the future of artificial intelligence and want to lead a team of elite engineers, this is your opportunity to leave a lasting legacy.
In this pivotal role, you will design and implement scalable deep learning architectures that power autonomous systems and generative AI solutions. You will bridge the gap between theoretical research and production-grade deployment, ensuring our systems are not only cutting-edge but also ethical and efficient.
Why Join Us?
- Impactful Work: Directly influence the architecture that will define the next era of AI.
- Top-Tier Team: Collaborate with Ph.D. researchers and industry veterans.
- Future-Proof: Work on projects specifically aligned with the 2026 strategic vision.
Responsibilities
- Architect and deploy scalable deep learning models for high-volume production environments.
- Lead the technical strategy for our 2026 AI roadmap, evaluating emerging technologies like Transformer architectures and reinforcement learning.
- Collaborate with cross-functional teams (Data Science, Product, Security) to integrate AI capabilities into consumer and enterprise products.
- Define best practices for model training, validation, and edge deployment to ensure minimal latency and maximum accuracy.
- Mentor senior engineers and conduct code reviews to maintain high engineering standards.
- Ensure AI systems adhere to ethical guidelines and regulatory compliance standards.
Qualifications
- Master’s or PhD degree in Computer Science, Artificial Intelligence, or a related technical field (or equivalent professional experience).
- 10+ years of experience in software engineering, with at least 5 years specifically in AI/ML architecture and deep learning.
- Extensive experience with Python, PyTorch, TensorFlow, and model serving frameworks.
- Proven track record of leading large-scale machine learning projects from conception to production.
- Strong understanding of distributed systems, cloud infrastructure (AWS/GCP), and MLOps pipelines.
- Exceptional communication skills with the ability to translate complex technical concepts for non-technical stakeholders.