Job Description
We are looking for a visionary Senior AI Engineer to join our elite team at Nexus Horizon Systems. As we accelerate our roadmap for 2025, we are building the next generation of intelligent, scalable, and ethical artificial intelligence solutions. You will be at the forefront of innovation, working on large-scale machine learning models, natural language processing, and generative AI architectures that power our global platform.
Why Join Us?
At Nexus Horizon, we don't just use AI; we define its future. You will have the autonomy to shape technical strategy, mentor a diverse team of engineers, and deploy models that impact millions of users. We offer a competitive salary, equity packages, and a culture that prioritizes continuous learning and cutting-edge research.
Responsibilities
- Model Development: Design, train, and fine-tune state-of-the-art machine learning models, including LLMs and Computer Vision systems, optimized for production environments.
- Infrastructure Optimization: Build and maintain robust MLOps pipelines to ensure model scalability, reliability, and real-time inference capabilities.
- Research & Innovation: Stay ahead of the curve in AI research, evaluating and implementing new architectures and algorithms to drive product differentiation.
- Code Quality: Write clean, efficient, and well-documented code; conduct code reviews to maintain high engineering standards across the team.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and backend engineers to translate business requirements into technical AI solutions.
Qualifications
- Education: M.S. or Ph.D. in Computer Science, Mathematics, Statistics, or a related field.
- Experience: 5+ years of professional experience in machine learning engineering or AI research roles.
- Programming: Expert proficiency in Python, with deep experience in frameworks such as PyTorch, TensorFlow, or JAX.
- System Design: Strong understanding of distributed systems, cloud computing (AWS/Azure/GCP), and containerization technologies (Docker, Kubernetes).
- Tools: Familiarity with MLflow, Airflow, and version control systems (Git).