Job Description
Join the pioneers of the 2026 AI revolution.
Nexus Future AI is on a mission to build the foundational models that will power the autonomous workforce of tomorrow. We are seeking a visionary Senior AI Architect to lead the development of next-generation Large Language Models (LLMs) and multimodal reasoning systems.
In this role, you will not just use existing tools; you will push the boundaries of what is possible in Generative AI, focusing on agentic workflows, real-time inference optimization, and scalable infrastructure for the year ahead.
Why join us?
• Work on state-of-the-art technology shaping the future.
• Competitive equity package and top-tier compensation.
• Flexible remote-first culture with a premium San Francisco office.
Responsibilities
- Architect & Deploy: Design and implement scalable AI infrastructure for high-volume, low-latency inference pipelines.
- Model Development: Spearhead the fine-tuning and alignment of large language models using custom datasets to enhance reasoning capabilities.
- RAG Systems: Build robust Retrieval-Augmented Generation (RAG) architectures to ensure knowledge accuracy and reduce hallucinations.
- Optimization: Perform rigorous optimization of model weights and quantization strategies to deploy on edge devices.
- Cross-Functional Leadership: Collaborate with product and engineering teams to translate complex AI concepts into user-centric features.
- Research: Stay ahead of the curve on emerging AI paradigms, including chain-of-thought reasoning and self-supervised learning.
Qualifications
- Experience: 5+ years of professional experience in software engineering or machine learning, with at least 3 years specifically focused on Generative AI or Deep Learning.
- Technical Skills: Proficiency in Python, PyTorch, and TensorFlow; experience with Hugging Face Transformers and LangChain.
- Model Engineering: Deep understanding of LLM architectures (Transformer, GPT, BERT variants) and the training lifecycle (pre-training, fine-tuning, RLHF).
- System Design: Strong ability to design distributed systems capable of handling petabyte-scale data and billions of parameters.
- Tools: Experience with Kubernetes, Docker, and cloud platforms (AWS/GCP/Azure) for model serving.
- Education: BS, MS, or PhD in Computer Science, Mathematics, or a related quantitative field.