Job Description
We are on the cusp of a technological revolution, and Nexus Future Systems is building the bridge to 2026. We are seeking a visionary Generative AI Architect to lead our next-generation research division. If you are passionate about pushing the boundaries of Large Language Models (LLMs), Neural Interfaces, and synthetic data generation, this is your opportunity to define the future of human-machine interaction.
In this role, you won't just maintain existing systems; you will architect the foundational intelligence that powers our ecosystem. Join a world-class team of researchers, engineers, and futurists dedicated to creating AI that is not only powerful but ethical, scalable, and profoundly impactful.
Responsibilities
- Design and implement state-of-the-art generative models and neural architectures capable of processing multimodal data streams.
- Lead research initiatives focused on reducing hallucination rates and improving reasoning capabilities in LLMs.
- Optimize inference latency and model efficiency to support real-time, edge-deployed AI applications.
- Collaborate with product teams to translate complex AI capabilities into user-centric features and tools.
- Establish best practices for data privacy, security, and ethical AI deployment within a cloud-native environment.
- Mentor junior researchers and engineers, fostering a culture of innovation and continuous learning.
Qualifications
- Master’s or Ph.D. degree in Computer Science, Artificial Intelligence, or a related quantitative field.
- 5+ years of professional experience building, deploying, and optimizing deep learning models at scale.
- Expert proficiency in Python, PyTorch, TensorFlow, and Hugging Face Transformers.
- Deep understanding of Natural Language Processing (NLP), Reinforcement Learning from Human Feedback (RLHF), and Vector Databases.
- Experience with cloud infrastructure (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Strong background in statistics, probability, and algorithm design with a focus on generative systems.