AI Principal Engineer
2 days ago
Barcelona
About the Company Our client is fast-scaling, SaaS technology company that allows companies to processes large volumes of unstructured data to deliver automated classification, filtering, and decision-making workflows for SMB clients. We handle billions of events and require models and systems that are highly scalable, robust, and production-grade. We are expanding our leadership and looking for a AI Principal Engineer who can own the technical direction, architecture, and productionization of our AI systems. Role Overview The AI Principal Engineer will be the technical leader responsible for designing, scaling, and delivering the company’s most critical AI systems. This includes image classification models , language safety / censorship / content filtering , and document classification from heterogeneous emails and sources . This is a highly strategic and hands-on role: part architecture, part research, part engineering, and part mentorship. You will define how AI is built, deployed, monitored, and scaled across the entire product. Key Responsibilities 1. Technical Leadership & Architecture 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 . Ensure best practices in MLOps, CI/CD for models, observability, safety, and reliability. Mentor and technically lead senior ML engineers, data scientists, and MLOps engineers. 2. Model Development Build and optimize image classification models (CNNs, ViTs, multimodal architectures). Develop language control and censorship models (toxicity detection, abuse filtering, safe text classification, LLM guardrails). Develop and improve document and email classification models , including layout-aware models, OCR pipelines, and multi-modal understanding. 3. AI for Large-Scale Production Own the strategy to scale AI models in production (distributed inference, caching strategies, GPU/CPU optimization, autoscaling). Guarantee low-latency inference, cost efficiency, and high-availability AI services. Drive continuous model improvement cycles with A/B testing, human feedback loops, and telemetry. 4. Collaboration & Cross-Functional Work Work closely with Product, Engineering, Data, and Security teams to align AI roadmap with business needs. Translate complex AI concepts into actionable plans for both technical and non-technical stakeholders. Collaborate on client-facing AI performance requirements and enterprise-grade SLAs. 5. AI Governance & Safety Lead the development of AI safety frameworks , guardrails, and content compliance. Ensure all models meet security, privacy, and ethical standards. Required Qualifications 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). Language Safety / Censorship / Toxicity / Content Moderation models . 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.