Job Description
We are pioneering the infrastructure for the year 2026 and beyond. Nexus Future Technologies is seeking a visionary Senior AI Architect & Future Tech Lead to architect scalable, ethical, and high-performance machine learning systems. If you want to define the roadmap for the next generation of artificial intelligence, this is your opportunity to lead from the front.
In this role, you will bridge the gap between cutting-edge research and production-grade engineering, ensuring our platforms are future-proof for the evolving landscape of 2026.
Why Join Us?
- Work on mission-critical AI infrastructure that scales globally.
- Competitive compensation and equity package.
- Flexible remote-first culture with office hubs in San Francisco.
- Access to the latest hardware for training next-gen models.
Responsibilities
- Architect End-to-End ML Pipelines: Design and implement robust data ingestion, processing, and model training pipelines optimized for high-volume production environments.
- Lead Model Strategy: Define the technical roadmap for Generative AI and Large Language Models (LLMs) tailored for the 2026 market demands.
- Optimize Inference: Reduce latency and cost of inference using techniques such as model quantization, distillation, and edge computing integration.
- Technical Leadership: Mentor a team of data scientists and engineers, conducting code reviews and architectural discussions to ensure best practices.
- Ethical AI Compliance: Implement fairness, transparency, and bias mitigation strategies in AI models to ensure responsible deployment.
- System Scalability: Collaborate with DevOps to ensure systems can handle petabyte-scale data growth and millions of concurrent requests.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related technical field.
- Experience: 7+ years of experience in software engineering and 4+ years in machine learning engineering.
- Core Skills: Proficiency in Python, PyTorch, TensorFlow, and SQL.
- Cloud Expertise: Deep experience deploying models on AWS, GCP, or Azure using Kubernetes and Docker.
- Algorithm Knowledge: Strong understanding of deep learning architectures, NLP, and computer vision algorithms.
- Problem Solving: Demonstrated ability to troubleshoot complex performance bottlenecks and optimize large-scale distributed systems.