Your tasks

• Solution Design: Develop comprehensive AI solution architectures for a variety of business problems, encompassing data pipelines, model training/deployment, infrastructure requirements, security considerations and general cost efficiency.
• Technical Leadership: Provide technical leadership and guidance to engineering teams throughout the AI solution lifecycle.
• Technology Evaluation: Evaluate and recommend appropriate AI technologies, tools, and platforms based on business needs and technical feasibility.
• System & Workflow Integration: Ensure seamless integration between AI solutions and existing Rohde & Schwarz systems.
• Scalability & Performance: Architect AI solutions for scalability, performance, and reliability, ensuring they can handle large volumes of data and user requests.
• ML / AI Lifecyle Management: Pro-active mindset to evolve standards and best practices for AI development, deployment and governance (e.g. MLOps, LLMOps, AgentsOps) across Rohde & Schwarz.
• Cost Optimization: Design cost-effective AI solutions, leveraging cloud resources and optimizing model performance to minimize infrastructure costs.
• Documentation & Standards: Develop and maintain comprehensive architectural documentation and establish standards for AI solution development and deployment.
• Stakeholder Management: Effectively communicate architectural designs and technical concepts to both technical and non-technical stakeholders.
• Team Leadership & Mentoring: Guide data scientists, ML engineers, and developers in implementing AI solutions and promote best practices in AI development and deployment.

Your qualifications

• Master’s degree in Computer Science, Software Engineering, or a related field. Architecture certification (e.g., TOGAF) is a plus.
• 8+ years of experience in software architecture and solution design, with at least 5+ years focused on AI/ML solutions.
• Strong understanding of machine learning concepts, algorithms, and techniques, including deep learning, natural language processing, and reinforcement learning.
• Deep understanding of common deep learning and neural network architectures (e.g., Transformers, Diffusion models, etc.
• Extensive experience designing and implementing AI solutions on cloud platforms. Cloud certifications are highly desirable.
• Expertise in data modeling, data warehousing, data lakes, and data pipeline design.
• Strong knowledge of integration patterns and technologies (e.g., APIs, microservices, message queues).
• Understanding of security principles and best practices for AI systems, data encryption, access control, and threat modeling.
• Experience with MLOps: Strong understanding of MLOps principles and experience with tools for automating the deployment, monitoring, and management of machine learning models.
• Experience in designing agentic AI platforms and workflows based on common agentic architectures and best practices.
• Experience with various vector database technologies (e.g., Pinecone, Weaviate, Chroma) and their integration into AI solutions.
• Solid understanding of DevOps principles and experience with CI/CD pipelines.
• Exceptional communication, presentation, and interpersonal skills, with the ability to articulate complex technical concepts to diverse audiences.

Interested?