Job Description
Are you ready to architect the intelligence of tomorrow? Nexus Future Systems is pioneering the next generation of autonomous agents and generative AI solutions for the year 2026 and beyond. We are seeking a visionary Senior AI Architect to lead the design and deployment of scalable, high-performance machine learning infrastructure.
In this role, you will bridge the gap between theoretical AI research and production-grade engineering. You will be instrumental in building systems that are not only robust but adaptable to the rapidly evolving landscape of Artificial General Intelligence (AGI). Join a team of elite engineers and researchers dedicated to solving humanity's most complex data challenges.
Why Join Us?
- Work on cutting-edge technology shaping the 2026 roadmap.
- Competitive compensation package including equity and bonuses.
- Flexible hybrid work environment in the heart of San Francisco.
- Access to state-of-the-art compute resources and research grants.
Responsibilities
- Design and implement scalable distributed machine learning pipelines for large-scale data processing.
- Lead the architecture of multi-modal AI models, integrating vision, language, and audio capabilities.
- Optimize model inference speeds and reduce latency for real-time applications.
- Collaborate with cross-functional teams to translate business requirements into technical AI solutions.
- Stay ahead of industry trends, specifically regarding agentic workflows and next-gen LLMs.
- Mentor junior engineers and establish best practices for AI code quality and deployment.
Qualifications
- PhD or Master’s degree in Computer Science, Mathematics, or a related field.
- 7+ years of experience in software engineering with a focus on Machine Learning and Artificial Intelligence.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Proven experience deploying models at scale using Kubernetes and cloud-native architectures (AWS/GCP).
- Strong understanding of Large Language Models (LLMs), fine-tuning, and Retrieval-Augmented Generation (RAG).
- Experience with MLOps tools such as MLflow, Kubeflow, or AWS SageMaker.