Job Description
Are you ready to define the future of technology? OmniFuture Systems is pioneering the architectural foundations for the 2026 era. We are looking for a visionary Senior AI Infrastructure Architect to lead our engineering team in building scalable, secure, and efficient AI systems that will power the next decade of digital innovation.
In this role, you will bridge the gap between theoretical AI research and practical, large-scale infrastructure deployment. You will be responsible for designing systems that are not only robust today but are future-proofed for the evolving standards of 2026 and beyond.
Why Join OmniFuture Systems?
- Impact: Work on core infrastructure that will be used by millions globally.
- Forward-Thinking: The focus is specifically on the 2026 roadmap, allowing you to shape technology trends before they become mainstream.
- Culture: A diverse, inclusive, and high-performance environment.
Responsibilities
- Architectural Vision: Lead the design and implementation of next-generation AI infrastructure, ensuring scalability and resilience for high-volume workloads.
- Roadmap Strategy: Define technical roadmaps aligned with the company's 2026 strategic goals, identifying emerging technologies like neuromorphic computing and edge AI.
- System Optimization: Oversee the deployment and tuning of Large Language Models (LLMs) and neural networks to ensure optimal latency and cost-efficiency.
- Security & Compliance: Implement rigorous security protocols and data governance frameworks compliant with future regulatory standards (GDPR 2026, etc.).
- Team Leadership: Mentor a team of senior engineers and architects, fostering a culture of innovation and technical excellence.
- Cross-Functional Collaboration: Partner with product managers and data scientists to translate business requirements into technical specifications.
Qualifications
- Experience: 8+ years of experience in software architecture, with at least 4 years specifically focused on AI/ML infrastructure or high-scale distributed systems.
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
- Technical Stack: Proficiency in Python, Go, or Rust; experience with Kubernetes, Docker, and cloud platforms (AWS/GCP/Azure).
- AI Expertise: Deep understanding of MLOps, model serving, and optimization techniques.
- Problem Solving: Demonstrated ability to solve complex, ambiguous problems with innovative solutions.
- Communication: Excellent verbal and written communication skills, capable of presenting technical concepts to non-technical stakeholders.