Job Description
Nexus Horizon Technologies is leading the charge into the future. We are building the foundational technologies for the year 2026 and beyond, focusing on next-generation Artificial Intelligence and autonomous systems. We are looking for a visionary Senior AI & Machine Learning Engineer to join our elite R&D division.
In this pivotal role, you will not be maintaining legacy systems; you will be architecting the future. You will lead the development of large-scale neural networks, generative models, and ethical AI frameworks designed to solve complex global challenges by 2026. If you are passionate about pushing the boundaries of what is possible in machine learning, this is your opportunity to shape the next decade of innovation.
Responsibilities
- Architect and deploy scalable deep learning models focused on 2026 AI capabilities, including Generative Adversarial Networks (GANs) and Transformer models.
- Lead research initiatives into next-generation Natural Language Processing (NLP) and Computer Vision to enhance autonomous decision-making systems.
- Optimize model training pipelines and inference speeds for high-volume production environments using distributed computing.
- Establish and enforce strict AI ethics, safety, and fairness standards across all development projects.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and innovation.
- Collaborate with product and engineering teams to integrate AI solutions into consumer and enterprise products.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
- 5+ years of professional experience designing, training, and deploying production-grade ML models.
- Expert proficiency in Python, TensorFlow, PyTorch, and CUDA.
- Strong experience with MLOps, CI/CD pipelines, and cloud infrastructure (AWS, GCP, or Azure).
- Proven track record of publishing research papers or holding patents in the field of AI/ML.
- Deep understanding of statistics, probability, and algorithm design.