Job Description
Nexus Future Technologies is at the forefront of the technological singularity, building the autonomous systems that will define the year 2026 and beyond. We are seeking a visionary 2026 AI Systems Architect to lead the architectural design of our next-generation neural frameworks. In this role, you will bridge the gap between theoretical artificial general intelligence (AGI) concepts and scalable, production-ready infrastructure.
Your mission is to engineer the core algorithms that power our predictive analytics engine, ensuring high-performance, low-latency processing across global clusters. You will be working in a high-pressure, innovative environment where your work will directly influence the trajectory of human-machine interaction.
Responsibilities
- Architect Design: Design and implement high-performance neural network architectures optimized for 2026 computing standards, including edge-to-cloud integration.
- Performance Tuning: Optimize model inference speeds and reduce latency for real-time decision-making systems.
- Autonomous Agents: Develop frameworks for self-evolving AI agents capable of complex, multi-step reasoning without human intervention.
- Infrastructure Scaling: Oversee the deployment of GPU clusters and distributed computing resources to handle petabyte-scale data streams.
- Ethical AI Governance: Establish and enforce protocols for safe AI alignment and bias mitigation in large language models.
- R&D Leadership: Collaborate with quantum computing research teams to explore hybrid quantum-classical algorithms for next-gen processing.
Qualifications
- Education: Master’s or Ph.D. in Computer Science, Mathematics, or a related technical field from a top-tier institution.
- Experience: 8+ years of experience in AI/ML engineering, with at least 3 years in a lead architecture role.
- Technical Stack: Proficiency in Python, Rust, and C++. Deep expertise in PyTorch, TensorFlow, and Hugging Face Transformers.
- System Design: Proven track record of designing scalable, fault-tolerant distributed systems (Kubernetes, Docker, AWS/GCP).
- Model Optimization: Extensive experience in model quantization, pruning, and deploying models on specialized hardware (TPUs, NPUs).
- Innovation: Demonstrated ability to research and implement cutting-edge techniques in Generative AI and Reinforcement Learning.