Job Description
We are looking for a visionary Senior AI Engineer to architect the next generation of intelligent systems for Nexus Horizon. In 2026, AI is no longer just about automation; it's about agentic intelligence and human-AI symbiosis. You will be at the forefront of developing Large Language Models (LLMs) and multimodal agents that redefine user interaction.
As a key member of our R&D division, you will bridge the gap between theoretical machine learning and production-grade software engineering. We offer a competitive compensation package, equity packages, and the opportunity to work with state-of-the-art hardware and frameworks.
Responsibilities
- Architect and Deploy: Design and implement scalable, high-performance AI infrastructure capable of handling petabyte-scale data processing.
- Model Optimization: Fine-tune proprietary LLMs to improve reasoning capabilities, reduce hallucinations, and enhance latency.
- Agentic Systems: Build autonomous agents that can plan, execute, and self-correct complex tasks within enterprise environments.
- Research & Development: Stay ahead of the curve by integrating emerging technologies like Reinforcement Learning from Human Feedback (RLHF) and Federated Learning.
- Collaboration: Partner with cross-functional teams (Product, Design, Data Science) to translate complex technical requirements into user-centric features.
- Code Review: Mentor junior engineers and maintain high standards of code quality, testing, and documentation.
Qualifications
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing.
- Programming: Proficiency in Python, with deep expertise in PyTorch or TensorFlow.
- Education: M.S. or Ph.D. in Computer Science, Artificial Intelligence, or a related field.
- Cloud Mastery: Proven track record of deploying models on AWS, GCP, or Azure using Docker and Kubernetes.
- Mathematical Foundation: Strong understanding of linear algebra, calculus, and probability statistics.
- Problem Solving: Ability to troubleshoot complex system bottlenecks and optimize model inference speeds.