Job Description
Architecting the Future of Intelligence
Nexus Horizon Solutions is at the forefront of defining the technology stack for the 2026 era. We are seeking a visionary Lead AI & Machine Learning Architect to spearhead the development of next-generation neural architectures and scalable AI systems. This is a rare opportunity to work on cutting-edge projects that will set the industry standard for the next decade.
In this pivotal role, you will bridge the gap between advanced theoretical research and robust production engineering, ensuring our solutions are future-proof, energy-efficient, and ethically sound.
Responsibilities
- Define the 2026 Technology Roadmap: Lead the strategic vision for AI infrastructure, identifying emerging trends such as Neuromorphic computing and Quantum AI interfaces to ensure long-term scalability.
- Design Distributed ML Systems: Architect high-throughput, fault-tolerant machine learning pipelines capable of processing petabytes of data in real-time.
- Pioneer Generative AI Solutions: Develop and fine-tune Large Language Models (LLMs) and multimodal models tailored for enterprise-grade automation.
- Optimize Computational Efficiency: Implement energy-efficient algorithms to minimize the carbon footprint of AI operations, aligning with global sustainability goals.
- Lead Technical Innovation: Mentor a team of engineers and data scientists, fostering a culture of continuous learning and experimentation.
Qualifications
- Advanced Education: PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field.
- Deep Expertise: Proven track record of 7+ years in machine learning engineering, with significant experience in Deep Learning, NLP, and Reinforcement Learning.
- Technical Mastery: Proficiency in Python, PyTorch, TensorFlow, and modern data processing frameworks (Apache Spark, Kafka).
- Cloud & Infrastructure: Extensive experience designing and deploying systems on AWS, GCP, or Azure using Kubernetes and microservices architecture.
- Futuristic Skills: Familiarity with research in edge computing, federated learning, or explainable AI (XAI) is a major plus.