Job Description
We are seeking a visionary Senior AI/ML Engineer to join our elite research division at Nexus Future Systems. In this role, you will be at the forefront of developing next-generation Artificial Intelligence solutions that will define the technological landscape of 2026 and beyond. You will work on cutting-edge Large Language Models (LLMs), Generative AI architectures, and autonomous systems that push the boundaries of what is possible.
At Nexus, we don't just build software; we engineer the future. If you are passionate about solving complex problems and possess a deep understanding of neural networks and data science, we want to hear from you.
Responsibilities
- Model Development: Design, train, and deploy state-of-the-art deep learning models, including Transformers and diffusion models, to solve real-world business problems.
- Optimization: Implement aggressive model compression and quantization techniques to ensure high-performance inference at scale, reducing latency for end-users.
- MLOps: Build and maintain robust CI/CD pipelines for machine learning, ensuring reproducibility and automated deployment of models to production environments.
- Data Strategy: Lead the architecture of large-scale data pipelines, focusing on data ingestion, processing, and feature engineering for training datasets.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and software engineers to translate business requirements into technical AI solutions.
- Research: Stay ahead of the curve by researching emerging AI trends, publishing papers, and contributing to open-source communities.
Qualifications
- Education: Master’s or Ph.D. degree in Computer Science, Mathematics, Statistics, or a related field.
- Experience: Minimum of 5+ years of professional experience in machine learning, deep learning, or a related field.
- Technical Skills: Expert proficiency in Python, PyTorch, or TensorFlow; solid foundation in statistics and linear algebra.
- Architecture: Deep understanding of distributed systems, cloud computing (AWS, GCP, or Azure), and containerization technologies (Docker, Kubernetes).
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders and cross-functional teams.
- Problem Solving: Demonstrated track record of troubleshooting complex algorithmic issues and optimizing system performance.