AI Architect bei Aleph

We are seeking an exceptional Lead AI Architect to own the strategy and execution of our most complex AI-powered products. You will design, architect, and guide the development of end-to-end AI systems, from foundational model selection and training to robust deployment and MLOps pipelines. This senior role requires a rare blend of strategic systems thinking, hands-on model development expertise, and deep technical mentorship. You will define the AI roadmap for major products, transforming ambiguous challenges into intuitive, scalable, and impactful solutions that deliver measurable business value.

Responsibilities:

Lead Complex Projects: Take full ownership of the AI solution for large-scale, ambiguous, and strategically important projects from discovery to launch.

Own End-to-end AI Development: Own the entire lifecycle for AI models, including independent development, training, tuning, and ongoing performance monitoring for specific product features.

Define Strategy: Influence the product roadmap and AI strategy by defining architecture, data flows, and interaction paradigms for AI systems.

Create High-Fidelity Mockups: Design and deliver system design and solution mockups and prototypes that clearly communicate the intended user experience for AI applications.

Facilitate & Mentor: Mentor junior AI developers on complex technical challenges, model optimization, and best-in-class software engineering practices.

Advocate for Users: Collaborate closely with software and data engineers to seamlessly integrate deployed AI models into production applications and monitor their performance.

Stakeholder Management: Confidently present and defend architectural and design decisions for AI systems to stakeholders and senior leadership.

Required Skills:

Expert-level command of AI/ML Design and Architecture: Deep knowledge of AI architecture, machine learning models, data science, and the ability to translate complex business requirements into tangible technical AI solutions.

Full-Stack Model Proficiency: Ability to independently develop, train, and tune a variety of AI models. Proficient in implementing various neural network architectures (e.g., CNNs, RNNs, Transformers).

MLOps Mastery: Deep understanding of model optimization, deployment, and MLOps principles. Proven experience designing and maintaining AI models in production environments.

Cloud & Engineering: Experienced with major cloud computing platforms (e.g., AWS, GCP, Azure). Strong software engineering principles and code versioning skills (e.g., Git).

Data & Ethics: Experience with data analysis and A/B testing for AI models. Familiarity with ethical AI guidelines and responsible AI practices.

Soft Skills: Strategic thinking, leadership and mentoring, exceptional communication and presentation skills, ability to influence and persuade.

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