Job Description
Are you ready to define the trajectory of Artificial Intelligence for the year 2026?
Nexus Future Systems is seeking a visionary Senior AI Architect to lead our next-generation model development. We are not just building software; we are architecting the cognitive layer of the future enterprise. In this role, you will be at the forefront of innovation, designing scalable, ethical, and powerful AI systems that will define the technological landscape of the coming decade.
You will collaborate with a world-class team of researchers, engineers, and product designers to push the boundaries of what is possible with Machine Learning and Generative AI. If you thrive in a high-impact environment and are passionate about solving complex problems at scale, we want to hear from you.
Responsibilities
- Lead the architectural design and implementation of large-scale machine learning models and generative AI systems.
- Drive the research and development of novel algorithms to improve model accuracy, efficiency, and fairness.
- Collaborate with cross-functional teams to integrate AI solutions into production environments, ensuring seamless scalability and performance.
- Mentor junior engineers and data scientists, fostering a culture of technical excellence and continuous learning.
- Establish best practices for data governance, model deployment, and MLOps pipelines.
- Stay ahead of the curve on emerging AI trends and technologies to ensure our roadmap remains competitive.
Qualifications
- Master’s degree or Ph.D. in Computer Science, Mathematics, Statistics, or a related technical field.
- 8+ years of professional experience in software engineering, with at least 5 years specifically in AI/ML architecture.
- Expert proficiency in Python and deep learning frameworks such as TensorFlow, PyTorch, or JAX.
- Proven experience designing and deploying Large Language Models (LLMs) or similar generative AI architectures.
- Strong understanding of distributed systems, cloud infrastructure (AWS, GCP, or Azure), and containerization technologies (Docker, Kubernetes).
- Experience with MLOps tools and CI/CD pipelines for machine learning models.
- Exceptional problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.