Job Description
Join the Future of Intelligence at Nexus Future Tech.
We are on a mission to build the world's most advanced, ethical, and efficient Generative AI systems. As a Senior AI Engineer, you will lead the development of cutting-edge Large Language Models (LLMs) and multimodal systems that redefine user interaction. We are looking for a visionary engineer to shape the technology stack that will power the digital landscape of 2026 and beyond.
Why Join Us?
- Work with state-of-the-art hardware and open-source frameworks.
- Collaborate with world-class researchers and engineers.
- Competitive equity package and flexible remote-first culture.
The Role:
You will own the end-to-end lifecycle of our AI models, from research and prototyping to deployment at scale. You will bridge the gap between theoretical research and production-grade code, ensuring our AI solutions are robust, efficient, and scalable.
Responsibilities
- Model Development: Design, train, and fine-tune large-scale transformer models and diffusion models using Python and PyTorch/TensorFlow.
- Optimization: Implement advanced optimization techniques including quantization, pruning, and distillation to improve inference speed and reduce latency.
- MLOps: Build and maintain CI/CD pipelines for machine learning, leveraging tools like Kubeflow or MLflow to automate model training and evaluation.
- Evaluation: Establish rigorous evaluation metrics and benchmarks to measure model performance, accuracy, and safety.
- Cross-Functional Collaboration: Partner with Product Managers and Data Scientists to define technical requirements and deliver impactful features.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field, with a focus on Machine Learning or Natural Language Processing.
- Experience: 5+ years of professional experience in AI/ML engineering, with specific expertise in Large Language Models (LLMs).
- Technical Skills: Proficiency in Python, C++, or CUDA. Deep knowledge of frameworks like PyTorch, TensorFlow, or Hugging Face Transformers.
- Infrastructure: Strong understanding of cloud infrastructure (AWS, GCP, or Azure) and distributed computing systems.
- Problem Solving: Demonstrated ability to tackle complex mathematical and algorithmic challenges with innovative solutions.