Job Description
The Opportunity: Nexus Future Systems is at the forefront of technological innovation, defining the roadmap for the year 2026 and beyond. We are seeking a visionary Lead AI Architect to design and implement scalable, next-generation artificial intelligence infrastructure. In this role, you will bridge the gap between theoretical research and practical application, ensuring our AI solutions are robust, ethical, and future-proof.
Why Join Us?
- Work on cutting-edge Generative AI and Large Language Models (LLMs).
- Shape the technical strategy for our flagship products.
- Competitive compensation and equity packages.
- Collaborate with world-class engineers and researchers.
Are you ready to architect the future? Apply today.
Responsibilities
- Architect and design scalable Machine Learning pipelines and infrastructure for 2026 and beyond.
- Lead a high-performing team of data scientists and AI engineers to deliver production-ready models.
- Define and enforce best practices for code quality, model deployment, and MLOps.
- Collaborate with product managers and stakeholders to translate business requirements into technical solutions.
- Conduct R&D to explore emerging technologies (e.g., Transformer architectures, reinforcement learning).
- Ensure AI systems adhere to ethical guidelines and data privacy regulations.
- Optimize model performance and reduce inference latency for real-time applications.
Qualifications
- Master’s or Ph.D. in Computer Science, Machine Learning, or a related field.
- 10+ years of experience in software engineering, with at least 5 years in AI/ML architecture.
- Deep expertise in Python, TensorFlow, PyTorch, or similar deep learning frameworks.
- Strong understanding of distributed systems, cloud architecture (AWS, GCP, or Azure), and containerization (Docker/Kubernetes).
- Proven experience leading cross-functional teams and mentoring junior engineers.
- Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.
- Familiarity with ethical AI principles and responsible machine learning practices.