Principal AI & Software Architect

The Principal AI & Software Architect serves as the core technical authority behind the SylvarisAI platform, owning the end-to-end architecture across AI systems, software platforms, and cloud infrastructure. This role requires a deeply hands-on architect with a strong background in machine learning and deep learning engineering, capable of designing, building, training, and deploying production AI systems at scale.

• Own the full system architecture of the SylvarisAI platform, including AI/ML system architecture, backend platform services, data pipelines, and cloud infrastructure.
• Design architectures that scale across large engineering datasets, high-throughput AI inference, and distributed compute environments.
• Remain actively involved in building and shipping production systems, implementing critical-path components when complexity or risk is high.
• Define and evolve the architecture behind SylvarisAI's AI-driven engineering platform, including deep learning model architectures and LLM engineering.
• Lead the design of production-grade ML/DL systems, including large-scale model training pipelines and full parameter fine-tuning of deep learning models.
• Design AI systems capable of processing complex engineering data such as verification logs, waveform data, and debugging traces.
• Apply architectural experience to proactively avoid failure modes in distributed systems, AI-heavy platforms, and cloud-native architectures.
• Act as the technical reference point for complex engineering decisions and mentor engineers across AI systems, backend platforms, and cloud infrastructure.
• Define and maintain architectural standards across the SylvarisAI platform and evaluate new technologies with focus on production readiness.
  

• 15 years of experience in software engineering, AI systems, or architecture roles.
• Strong hands-on background in machine learning and deep learning engineering.
• Proven experience designing and deploying production AI/ML systems at scale.
• Deep expertise in modern deep learning architectures, large-scale model training, and full parameter fine-tuning.
• Experience with ML model lifecycle management and distributed systems.
• Demonstrated experience with LLM fine-tuning, building scalable ML pipelines, and operating AI systems in production.
• Experience training or fine-tuning models on GPU clusters using distributed training frameworks.
• Clear track record of scaling complex AI platforms and leading architectural decisions.
  

• Experience building AI-powered engineering tools or developer platforms.
• Familiarity with semiconductor design or verification workflows.
• Experience with agent-based or LLM-driven architectures.
• Experience working in deep-tech or infrastructure startups.