Job Description
We are building the infrastructure for the next generation of artificial intelligence, and we need a visionary Architect to lead our 2026 roadmap. At Nexus Horizon Technologies, we are not just predicting the future of AI; we are engineering it. We are seeking a Senior AI Architect with a passion for generative models, scalable infrastructure, and the cutting-edge challenges that define the year 2026.
In this pivotal role, you will define the technical strategy for our flagship generative AI suite, bridging the gap between theoretical research and production-grade deployment. You will work alongside world-class researchers and engineers to solve complex problems in natural language processing, computer vision, and autonomous decision-making systems.
Why Join Us?
- Shape the trajectory of AI technology for the next decade.
- Work with state-of-the-art hardware (H100 clusters, TPUs).
- Competitive compensation package with equity.
If you are ready to architect the future, we want to hear from you.
Responsibilities
- Architect LLM Pipelines: Design and implement scalable, high-throughput pipelines for Large Language Models (LLMs) and multimodal systems.
- Model Optimization: Apply quantization, pruning, and distillation techniques to deploy massive models on edge and cloud devices efficiently.
- System Design: Lead the design of MLOps infrastructure, ensuring reliability, security, and data governance for proprietary datasets.
- Research Integration: Evaluate and integrate novel research papers into production environments, bridging the gap between academia and industry.
- Performance Tuning: Continuously monitor and optimize inference latency and token generation speed to ensure real-time user experiences.
- Team Leadership: Mentor junior engineers and conduct code reviews to maintain high technical standards across the AI engineering team.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related technical field.
- Experience: 5+ years of professional experience in machine learning engineering or AI research.
- Core Tech Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX.
- Deep Learning: Strong understanding of transformer architectures, attention mechanisms, and fine-tuning strategies (PEFT, LoRA).
- MLOps: Experience with Kubernetes, Docker, and cloud platforms (AWS, GCP, or Azure) specifically for ML workloads.
- Problem Solving: Ability to troubleshoot complex distributed systems and optimize GPU resource utilization.
- Communication: Excellent written and verbal communication skills for technical documentation and stakeholder presentations.