AI Principal Engineer - Scalable ML Systems & Strategy

• Define the AI system architecture end-to-end (training pipelines, inference, real-time processing, monitoring, auto-scaling).
• Lead the design of distributed ML systems that support massive scale production workloads.
• Build and optimize image classification models (CNNs, ViTs, multimodal architectures).
• Own the strategy to scale AI models in production (distributed inference, caching strategies, GPU/CPU optimization, autoscaling).
• Work closely with Product, Engineering, Data, and Security teams to align AI roadmap with business needs.
• Lead the development of AI safety frameworks, guardrails, and content compliance.

A fast-scaling SaaS technology company is looking for an AI Principal Engineer to spearhead the technical direction and architecture of their AI systems. Responsibilities include designing and delivering scalable AI models, leading a team of engineers, and ensuring AI governance and safety. The successful candidate will bring over 7 years of ML/AI engineering experience, with strong skills in image classification, document processing and MLOps. A strategic mindset and ability to thrive in a fast-paced environment are essential., * 7+ years of experience in ML/AI engineering, with at least 3 years in senior/lead/principal roles.

• Demonstrated expertise in image classification models (CNNs, Vision Transformers, multi-class, multi-label).
• Experience with language safety/censorship/toxicity/content moderation models.
• Experience in document classification (emails, PDFs, structured/unstructured documents).
• Design of scalable AI production systems (microservices, distributed inference, parallel training).
• Strong track record deploying AI models at scale (millions of daily requests).
• Solid background in MLOps, including feature stores, pipelines, orchestration, monitoring, and versioning.
• Strong communication skills and ability to lead technical discussions.
• Strategic mindset with hands-on execution capability.
• Ability to operate in fast-paced, high-growth technology environments.
• Degree in Computer Science, AI, Machine Learning, or related field; MSc or PhD preferred but not required if compensated by strong experience.