Job Description
We are seeking a visionary Senior AI/ML Architect to lead our Project 2026 initiative. Nexus Future Systems is building the next generation of autonomous AI agents, and we need a technical leader who can architect scalable, ethical, and high-performance machine learning systems. This role is critical in defining the infrastructure that will power the future of enterprise automation.
In this position, you will bridge the gap between cutting-edge research and production engineering, ensuring our models are not only accurate but also safe, efficient, and ready for global deployment.
Responsibilities
- System Architecture: Design and implement robust, scalable ML infrastructure for large-scale model training and inference.
- Model Optimization: Fine-tune proprietary LLMs and Transformer architectures to maximize throughput and minimize latency in real-time environments.
- R&D Leadership: Lead the research and development of novel neural network architectures and reinforcement learning strategies.
- Ethical AI & Safety: Implement rigorous testing and validation protocols to ensure AI outputs are unbiased, transparent, and compliant with emerging regulations.
- DevOps Integration: Oversee CI/CD pipelines, MLOps tools, and container orchestration (Kubernetes/Docker) to streamline model deployment.
- Team Mentorship: Guide junior engineers and data scientists, fostering a culture of innovation and technical excellence.
Qualifications
- Education: Master’s degree or PhD in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: 5+ years of professional experience in machine learning engineering, with a proven track record of deploying models to production.
- Core Skills: Expert proficiency in Python, PyTorch, TensorFlow, and Hugging Face.
- Cloud Expertise: Deep experience with AWS, GCP, or Azure, specifically in setting up GPU clusters and serverless inference.
- Problem Solving: Strong analytical skills with the ability to debug complex distributed systems and optimize resource utilization.
- Soft Skills: Excellent communication skills with the ability to translate complex technical concepts for diverse stakeholders.