Job Description
We are on the precipice of a technological renaissance. As we look toward 2026, Nexus Future Labs is seeking a visionary Senior AI & Machine Learning Architect to lead our next generation of cognitive systems.
In this pivotal role, you won't just be building models; you will architect the digital intelligence that will define the future of our industry. You will be working with state-of-the-art frameworks, pushing the boundaries of Generative AI, and deploying scalable solutions that impact millions.
If you are a technical leader passionate about the convergence of data science and human-centric design, we want to meet you.
Why Join Us?
- Future-Ready Environment: Work in a company building for the year 2026 and beyond.
- Elite Team: Collaborate with Ph.D. researchers and industry veterans.
- Equity & Growth: Competitive compensation package with significant equity opportunities.
Responsibilities
- Architect Scalable AI Solutions: Design and implement robust machine learning infrastructure capable of handling petabyte-scale data and high-velocity inference.
- Lead Research & Development: Spearhead R&D initiatives into cutting-edge topics such as Large Language Models (LLMs), reinforcement learning, and neural architecture search.
- Model Deployment: Oversee the end-to-end lifecycle of ML models from prototyping in Jupyter Notebooks to production-grade deployment using Kubernetes and Docker.
- Technical Mentorship: Mentor a team of junior data scientists and machine learning engineers, fostering a culture of innovation and continuous learning.
- Strategic Vision: Work closely with C-level executives to translate business goals into technical AI roadmaps.
Qualifications
- Education: Master’s or PhD degree in Computer Science, Mathematics, Statistics, or a related field.
- Experience: Minimum of 5-7 years of professional experience in machine learning engineering or applied AI research.
- Core Skills: Proficiency in Python, TensorFlow, PyTorch, and Scikit-learn.
- Architecture: Strong experience designing distributed systems and cloud-native applications (AWS, GCP, or Azure).
- Innovation: A proven track record of publishing papers or deploying novel AI solutions in production environments.