Job Description
Shape the Future of Intelligence for 2026
Nexus Horizon is pioneering the next generation of Artificial General Intelligence (AGI) and generative workflows. We are looking for a visionary Senior AI & Machine Learning Engineer to lead our research and development efforts. If you are passionate about building scalable, ethical, and transformative AI systems that will define the technological landscape of the next decade, we want to meet you.
In this role, you will not just implement existing models; you will architect the algorithms that power our predictive engines and autonomous systems. You will work at the intersection of theoretical computer science and practical deployment, ensuring our solutions are robust, secure, and ready for enterprise scale.
Responsibilities
- Architect & Develop: Design and implement advanced machine learning pipelines and neural network architectures to solve complex, real-world problems.
- Model Optimization: Continuously optimize model performance, accuracy, and latency for high-traffic production environments.
- Research & Innovation: Stay at the forefront of AI trends, researching and integrating cutting-edge techniques (e.g., Transformers, Reinforcement Learning) into our core stack.
- Collaboration: Partner with cross-functional teams of data scientists, software engineers, and product managers to translate research into production-ready features.
- MLOps & Deployment: Manage the end-to-end machine learning lifecycle, including data ingestion, model training, evaluation, and deployment using CI/CD pipelines.
- Ethical AI: Ensure AI models are fair, transparent, and compliant with evolving regulatory standards and ethical guidelines.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, Mathematics, or a related quantitative field (or equivalent practical experience).
- Programming: Deep expertise in Python, PyTorch, or TensorFlow; strong proficiency in C++ for high-performance computing.
- Experience: 5+ years of professional experience in building and deploying large-scale machine learning systems.
- Cloud Mastery: Proven experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Algorithms: Solid understanding of statistical modeling, optimization techniques, and distributed systems.
- Communication: Excellent ability to communicate complex technical concepts to both technical and non-technical stakeholders.