Job Description
Join Nexus Future Systems, a pioneering force in next-generation artificial intelligence, as we architect the solutions for the 2026 era. We are looking for a visionary Senior AI Architect to lead the design and implementation of scalable, high-performance machine learning systems. You will be at the forefront of innovation, shaping the intelligence that drives our products and defines our market presence.
In this role, you will bridge the gap between theoretical research and practical engineering, ensuring our AI models are robust, ethical, and ready for deployment in complex real-world environments.
Why you should apply:
- Impactful Work: Build the core AI infrastructure that will power our global platform.
- Future-Proof Career: Work on cutting-edge technologies that are defining the future.
- Generous Compensation: Top-tier salary and equity package for top-tier talent.
Responsibilities
- Design and architect end-to-end machine learning pipelines and data processing systems.
- Lead the development of advanced deep learning models, including NLP, Computer Vision, and Reinforcement Learning.
- Optimize model performance, latency, and scalability for cloud and edge deployment environments.
- Establish and enforce best practices for MLOps, model versioning, and continuous integration.
- Collaborate with cross-functional teams to translate business requirements into technical AI solutions.
- Ensure AI systems are compliant with ethical guidelines and data privacy regulations (GDPR/CCPA).
- Mentor junior engineers and data scientists, fostering a culture of technical excellence.
Qualifications
- Master’s or PhD degree in Computer Science, Mathematics, or a related technical field.
- Minimum of 5 years of professional experience in AI/ML engineering, with at least 2 years in a lead or architect role.
- Expert proficiency in Python, PyTorch, TensorFlow, and Scikit-learn.
- Deep understanding of transformer architectures, LLMs, and large-scale distributed training.
- Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies (Docker, Kubernetes).
- Proven track record of deploying production-grade AI models to millions of users.