IC4 - Sr AI Engineer

Objective We’re building an AI-first organization — and we’re looking for engineers who actually build, not just experiment. We’re hiring a Senior AI Engineer to design, develop, and scale AI-powered products across our ecosystem — from LLM-based customer experiences to internal copilots that transform how teams operate. You’ll be part of a centralized AI Products Chapter, embedding into squads to bring real AI solutions into production. What you’ll do - Build and deploy Generative AI solutions (LLMs, RAG, agents, copilots). - Design and implement scalable AI systems in production. - Work across the full lifecycle: prototype → deploy → optimize. - Integrate models with real systems (APIs, data platforms, workflows). - Create evaluation frameworks, monitoring, and feedback loops. - Partner with Product, Data, and Engineering to ship real impact. What we’re looking for - Strong software engineering background (Python + modern stack). - Hands-on experience with LLMs / Generative AI / NLP. - Experience building and deploying ML/AI systems in production. - Familiarity with cloud (AWS/GCP), APIs, and data pipelines. - Strong problem-solving and execution mindset. Why this role - You won’t just build models → you’ll ship real AI products. - You’ll work across multiple squads → high leverage impact. - You’ll help define how AI Engineering is done at scale. Required Knowledge & Experience - Experience: 7+ years in Data & Tech, with at least 3 years specifically focused on AI/ML and hands-on experience in senior technical roles. - Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Applied Mathematics, or a related field. - AI Expertise: Deep understanding of Deep Learning, Natural Language Processing (NLP), and Gen AI (LLMs, RAGs, Agents, etc.). - Technical Stack: Proficiency in Python (PyTorch, TensorFlow) and experience with AI development frameworks (LangChain, LlamaIndex, Hugging Face). - Cloud & Big Data: Experience in cloud-native AI services (AWS SageMaker or Google Vertex AI) and big data frameworks. - Infrastructure: Familiarity with vector databases (Pinecone, Milvus, Weaviate) and MLOps tools for model tracking. - Communication: Ability to explain complex AI concepts to non-technical stakeholders in a business-centric way. - Ethics & Compliance: Knowledge of AI governance, including data privacy and model interpretability. - Soft Skills: Proactive problem-solving, strategic thinking, and commitment to high-quality code and innovation. If you’re excited about building real AI products (not just talking about them), we should talk. 🚀 En Spin estamos comprometidos con construir un lugar de trabajo diverso e inclusivo. Creemos en la igualdad de oportunidades y promovemos un entorno libre de discriminación por motivos de raza, origen nacional, género, identidad de género, orientación sexual, discapacidad, edad o cualquier otra condición legalmente protegida.