Job Description
Are you ready to architect the future of intelligence?
Nexus Horizon Technologies is on the forefront of defining the technological landscape of 2026. We are seeking a visionary Senior AI Architect to spearhead our next-generation machine learning initiatives and build scalable systems that will power the next decade of innovation.
In this role, you will bridge the gap between theoretical research and production-grade deployment. You will lead a high-performing team of data scientists and engineers, ensuring our AI models are not only cutting-edge but also ethical, efficient, and scalable.
Why Join Us?
- Work on projects that redefine industry standards for Generative AI and Neural Networks.
- Competitive compensation and equity packages.
- Flexible remote-first culture with state-of-the-art equipment.
Responsibilities
- Lead System Architecture: Design and implement robust, scalable, and secure AI infrastructure and pipelines for large-scale data processing.
- Model Development: Spearhead the research and development of advanced machine learning models, focusing on Natural Language Processing (NLP) and Computer Vision.
- Technical Leadership: Mentor junior engineers and data scientists, conducting code reviews, and establishing best practices for AI engineering.
- Strategic Roadmapping: Collaborate with product leadership to define technical roadmaps and integrate AI capabilities into our core products.
- Performance Optimization: Continuously monitor model performance, optimize inference speeds, and reduce latency in real-time applications.
- Ethical AI Compliance: Ensure all AI systems adhere to regulatory standards and ethical guidelines regarding bias, privacy, and transparency.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, Mathematics, or a related field.
- Experience: 7+ years of professional experience in software engineering, with at least 4 years focused on AI/ML architecture and model deployment.
- Technical Skills: Deep expertise in Python, PyTorch, TensorFlow, or JAX. Proven experience with MLOps tools (MLflow, Kubeflow, Docker, Kubernetes).
- Frameworks: Strong understanding of deep learning architectures (Transformers, GANs, RNNs).
- Cloud Proficiency: Hands-on experience deploying models on AWS, Google Cloud, or Azure.
- Problem Solving: Demonstrated ability to solve complex technical problems and translate business requirements into technical solutions.