Job Description
We are at the precipice of a technological revolution. Quantum Future Systems is seeking a visionary AI Architect: 2026 Strategic Vision to lead our next-generation autonomous systems division. If you possess the foresight to build the infrastructure for tomorrow and the technical prowess to execute it today, we want you on our team.
In this role, you will bridge the gap between theoretical research and production-scale deployment. You won't just be maintaining models; you will be architecting the future of human-machine interaction, ensuring our systems remain scalable, ethical, and ahead of the curve.
Why Join Us?
- Future-Proof Your Career: Work on technologies that will define the decade.
- Equity Package: Competitive stock options in a high-growth unicorn.
- Flexible Environment: Hybrid work model with state-of-the-art facilities.
Responsibilities
- Design and implement scalable machine learning infrastructure capable of processing exabytes of data for the 2026 ecosystem.
- Lead the research and development of next-generation Generative AI models, focusing on efficiency and hallucination reduction.
- Collaborate with cross-functional teams (Product, Security, Engineering) to integrate AI solutions into core products.
- Define technical roadmaps and architectural standards for the AI department.
- Ensure model fairness, transparency, and compliance with evolving global regulations.
- Optimize existing neural networks for edge computing deployment.
Qualifications
- Master’s degree or PhD in Computer Science, Mathematics, or a related field (PhD preferred).
- Minimum of 5 years of professional experience in AI/ML engineering, with a focus on NLP or Computer Vision.
- Deep proficiency in Python, TensorFlow, PyTorch, and distributed computing frameworks (Kubernetes, Apache Spark).
- Proven track record of deploying large-scale models into production environments.
- Strong understanding of MLOps, data pipelines, and cloud architecture (AWS/GCP).
- Excellent communication skills with the ability to translate complex technical concepts for non-technical stakeholders.