Job Description
Are you ready to define the trajectory of artificial intelligence in the next decade? Nexus Future Labs is seeking a visionary Senior LLM Architect to spearhead the development of next-generation generative models. In this role, you will bridge the gap between theoretical research and scalable production engineering, ensuring our AI solutions are not only cutting-edge but ethically sound and highly efficient.
As we prepare for the widespread adoption of AI in 2026 and beyond, we need a leader who understands the nuances of Large Language Models, transformer architectures, and the future of human-AI interaction. Join us in building the intelligent systems that will power the world's most critical industries.
Responsibilities
- Architect Design: Design and implement scalable, high-performance Large Language Model architectures capable of handling complex, real-world data streams.
- R&D Leadership: Lead research initiatives to explore novel techniques in few-shot learning, reinforcement learning from human feedback (RLHF), and model compression.
- System Optimization: Oversee the optimization of inference latency and training throughput to ensure cost-effective deployment on cloud infrastructure.
- Ethical AI Governance: Establish and enforce guidelines for bias mitigation, data privacy, and safety in generative AI outputs.
- Cross-Functional Collaboration: Partner with product managers, data scientists, and engineers to translate technical roadmaps into actionable product features.
- Technical Mentorship: Mentor junior engineers and data scientists, fostering a culture of continuous learning and technical excellence within the AI division.
Qualifications
- Education: Master’s or PhD degree in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in machine learning engineering, with at least 2 years focused specifically on LLMs and NLP.
- Technical Skills: Deep expertise in PyTorch, TensorFlow, or JAX; strong proficiency in Python and distributed computing frameworks (e.g., Ray, Kubernetes).
- Modeling: Proven track record of training, fine-tuning, and deploying state-of-the-art transformer models (GPT, BERT, Llama architectures).
- Problem Solving: Demonstrated ability to troubleshoot complex architectural challenges and optimize model performance in production environments.
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders and leadership.