Job Description
Are you ready to architect the future of Artificial General Intelligence? Nexus Future Systems is seeking a visionary Senior AI Architect to join our elite engineering team in San Francisco. In this pivotal role, you will design the foundational infrastructure for next-generation Large Language Models (LLMs) and autonomous agents set to define the landscape of 2026 and beyond.
At Nexus Future Systems, we are not just building software; we are engineering the fabric of digital consciousness. You will work at the intersection of deep learning, distributed systems, and ethical AI governance. If you are passionate about pushing the boundaries of what is possible with AI, we want to hear from you.
Why Join Us?
- Work on cutting-edge Generative AI projects that redefine human-machine interaction.
- Competitive compensation package with equity options.
- Flexible remote-first culture with a hub in the heart of SF.
Responsibilities
- Design and deploy scalable, fault-tolerant AI architectures for production-grade Large Language Models (LLMs).
- Lead the research and implementation of fine-tuning strategies for specialized NLP tasks and multimodal models.
- Collaborate with data scientists and ML engineers to optimize model inference, latency, and cost efficiency.
- Establish best practices for AI model governance, safety, and ethical usage in autonomous systems.
- Oversee the integration of AI APIs into broader software ecosystems and developer platforms.
- Mentor junior engineers and define technical roadmaps for the AI infrastructure team.
Qualifications
- Ph.D. or Master’s degree in Computer Science, Mathematics, or a related technical field.
- 10+ years of experience in software engineering with a strong focus on machine learning infrastructure.
- Deep expertise in PyTorch, TensorFlow, and Hugging Face Transformers.
- Strong proficiency in distributed systems, microservices, and cloud platforms (AWS, GCP, or Azure).
- Proven track record of delivering large-scale machine learning projects from concept to production.
- Experience with MLOps, Kubernetes, and real-time data streaming technologies.