Job Description
Are you ready to define the technological landscape of 2026? Nexus Future Labs is seeking a visionary Senior AI Architect to spearhead our next generation of Generative Intelligence systems. We are building the foundational infrastructure for the autonomous web and are looking for a leader who can bridge the gap between theoretical AI research and scalable, production-grade software engineering.
In this pivotal role, you will architect the core of our proprietary Large Language Model (LLM) ecosystem, ensuring our models are not only state-of-the-art but also ethical, efficient, and safe. You will work in a dynamic environment where your code will directly impact millions of users globally. If you are passionate about the future of AI and want to be at the forefront of the 2026 technological revolution, we want to hear from you.
Responsibilities
- Design and implement advanced Retrieval-Augmented Generation (RAG) pipelines and vector databases to enhance model accuracy.
- Lead the end-to-end development of LLM fine-tuning workflows using Hugging Face and custom PyTorch implementations.
- Optimize model inference latency and throughput for high-scale distributed systems.
- Establish and enforce best practices for AI safety, bias mitigation, and responsible deployment.
- Collaborate with cross-functional teams (product, security, design) to translate complex AI capabilities into user-friendly features.
- Conduct research on emerging AI architectures, including Mixture of Experts (MoE) and state-space models.
Qualifications
- Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field.
- 7+ years of professional software engineering experience with at least 3 years specifically focused on AI/ML infrastructure.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Proven track record of deploying large-scale ML models to production environments (AWS, GCP, or Azure).
- Experience with prompt engineering, LLM orchestration frameworks (LangChain, LlamaIndex), and vector search.
- Strong understanding of deep learning architectures, transformers, and neural network optimization.