Job Description
Are you ready to architect the future of intelligence?
OmniFuture Systems is pioneering the technologies that will define the year 2026. We are looking for a visionary Senior AI Architect to lead the development of next-generation generative models, quantum-enhanced neural networks, and autonomous systems.
In this role, you won't just be writing code; you will be setting the strategic technical roadmap for the decade ahead. You will work at the intersection of deep learning, advanced algorithmic theory, and scalable infrastructure.
Why join us?
We offer competitive compensation, equity packages, and the unique opportunity to build the foundational technologies that will power the world of tomorrow.
Responsibilities
- Define the 2026 Technical Roadmap: Lead the architectural vision for our AI systems, anticipating future shifts in neural processing and data scalability.
- Architect Next-Gen Models: Design and deploy large-scale transformer and generative AI models optimized for edge computing and quantum hybrid environments.
- Lead High-Performance Engineering: Oversee a team of elite engineers to ensure code quality, system stability, and performance optimization at scale.
- Future-Proof Infrastructure: Build resilient cloud-native architectures capable of handling exabyte-scale data streams.
- Mentorship & Strategy: Mentor junior developers and collaborate with product leaders to translate futuristic concepts into executable technical strategies.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, or a related field (PhD preferred).
- Experience: 8+ years of professional experience in software engineering, with at least 5 years specifically in AI/ML architecture.
- Technical Mastery: Deep expertise in PyTorch, TensorFlow, or JAX; proven experience with distributed systems and high-performance computing.
- Quantum Readiness: Understanding of quantum computing principles and how they integrate with classical machine learning workflows.
- Problem Solving: Exceptional ability to solve complex, ambiguous problems and navigate the 'black box' of deep learning research.
- Communication: Ability to articulate complex technical concepts to non-technical stakeholders and executive leadership.