Job Description
Are you ready to define the technological landscape of 2026 and beyond? Nexus Future Systems is seeking a visionary Senior AI Architect to lead our next generation of Generative AI solutions. We are building the infrastructure that will power autonomous decision-making and creative intelligence at enterprise scale.
In this role, you won't just maintain systems; you will architect the future. You will work at the intersection of deep learning, natural language processing, and scalable cloud infrastructure, ensuring our models are not only powerful but ethical, efficient, and future-proof.
Why join us?
- Impactful Work: Directly influence the AI capabilities of Fortune 500 clients.
- Future-Proof Tech Stack: Work with the latest in LLMs, Transformer architectures, and Quantum-ready frameworks.
- Competitive Compensation: Top-tier salary and equity packages.
Responsibilities
- Architectural Leadership: Design and implement scalable, high-performance AI architectures for Generative AI models, ensuring low-latency inference and high throughput.
- Model Optimization: Spearhead research into model compression, quantization, and fine-tuning strategies to optimize resource usage and reduce costs.
- R&D Strategy: Stay ahead of the curve on emerging AI trends (e.g., Multimodal AI, Agentic Workflows) and evaluate their applicability to our product roadmap.
- Ethical AI Compliance: Establish and enforce guidelines for responsible AI, ensuring fairness, transparency, and safety in all deployed models.
- Technical Mentorship: Mentor a team of junior engineers and data scientists, fostering a culture of innovation and continuous learning.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Machine Learning, or a related quantitative field.
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or AI Engineering, with a specific focus on Large Language Models (LLMs).
- Technical Proficiency: Expert-level knowledge of Python, PyTorch, TensorFlow, or JAX. Proven experience deploying models via Kubernetes or similar containerization platforms.
- Mathematical Foundation: Strong grasp of linear algebra, calculus, and probability theory.
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems in ambiguous environments.