Job Description
We are building the foundation for the next decade of intelligent computing. Nexus Future Systems is looking for a visionary Lead AI Architect (Project 2026) to define the core intelligence of our upcoming ecosystem. If you are passionate about pushing the boundaries of Generative AI, Large Language Models (LLMs), and predictive analytics, this is your chance to lead a high-impact initiative.
As part of our elite R&D division, you will be responsible for architecting scalable, secure, and efficient machine learning pipelines. You will work directly with senior leadership to translate ambitious 2026 roadmap goals into concrete technical strategies. This role requires a unique blend of deep technical expertise, architectural foresight, and the ability to mentor world-class engineering talent.
Why Join Us?
- Competitive salary and equity package.
- Work on cutting-edge technology that will shape the future of enterprise AI.
- Flexible remote and hybrid work options.
Responsibilities
- Design and architect the core machine learning infrastructure for Project 2026, ensuring high availability and low latency.
- Lead a cross-functional team of data scientists, ML engineers, and backend developers to deliver production-ready AI solutions.
- Develop and optimize deep learning models and neural networks using Python and modern frameworks.
- Establish and enforce best practices for code quality, testing, and deployment (CI/CD pipelines).
- Conduct technical research to evaluate emerging AI technologies and integrate them into our stack.
- Communicate complex architectural decisions to stakeholders and non-technical team members effectively.
- Drive innovation in data governance and ethical AI usage.
Qualifications
- Master’s degree or PhD in Computer Science, Mathematics, Statistics, or a related field (PhD preferred).
- Minimum of 7 years of experience in software engineering, with at least 3 years in a Lead Architect or Senior AI Engineer role.
- Extensive proficiency in Python, PyTorch, TensorFlow, and scikit-learn.
- Strong experience with distributed systems, cloud platforms (AWS, GCP, or Azure), and containerization (Docker, Kubernetes).
- Deep understanding of MLOps, data pipelines, and model deployment strategies.
- Proven track record of leading engineering teams and managing large-scale projects.
- Excellent problem-solving skills and a passion for continuous learning in the AI space.