Lead AI Systems Architect (Data-Centric AI & Multimodal)

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead AI Systems Architect (Data-Centric AI & Multimodal) based in Switzerland.

This is an opportunity for an experienced AI engineer to design and scale advanced AI systems that transform unstructured social media content into high-quality, structured data products. You will lead the development of multimodal pipelines combining computer vision, speech recognition, and large language models. The role focuses on building reliable data extraction workflows, optimizing model performance, and improving the quality of large-scale datasets. You will also drive the adoption of open-source models and efficient inference infrastructure to improve scalability and control costs. Working at the intersection of applied AI, data engineering, and social media intelligence, you will help shape the technical architecture of an evolving AI and Data Lab. You will also provide technical guidance to junior developers and establish engineering practices that support sustainable growth.

As Lead AI Systems Architect, you will oversee the design, implementation, and optimization of AI-powered data processing systems, ensuring accuracy, efficiency, and scalability across complex multimodal workflows.

• Multimodal AI Pipeline Development: Design and build automated ingestion pipelines combining computer vision for scene classification and aesthetic scoring, speech-to-text technologies such as Whisper, and large language models.
• Structured Data Extraction: Develop programmatic extraction frameworks using tools such as Instructor, Pydantic, DSPy, and Outlines to ensure generative models produce consistent, validated JSON outputs.
• Model Optimization and Cost Management: Transition workloads from commercial AI APIs to fine-tuned open-source models, including Llama, Qwen, and DeepSeek, deployed through local or high-throughput inference engines such as vLLM, TGI, and Groq.
• Data Labeling and Quality Assurance: Implement data-centric AI techniques and frameworks, including Cleanlab, Snorkel, and active learning, to automate large-scale labeling processes and improve the quality of noisy datasets.
• AI Architecture and Technical Leadership: Define architectural roadmaps, establish engineering standards, and guide junior developers in building robust, maintainable AI systems.
• Scalable Data Infrastructure: Support the development of AI-driven enrichment workflows and retrieval systems capable of processing large volumes of unstructured content efficiently.

Requirements:

The ideal candidate brings strong applied AI expertise, hands-on experience with multimodal systems, and the ability to translate advanced machine learning techniques into reliable production solutions.

• At least 5 years of experience in applied AI, natural language processing, or computer vision.
• Proven experience designing and implementing multimodal pipelines that integrate vision, speech, and text models.
• Advanced proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Strong experience with production LLM orchestration and structured output tools, including DSPy, Instructor, Pydantic, LangChain, or LlamaIndex.
• Hands-on experience fine-tuning and serving open-source models using vLLM, TGI, or comparable high-throughput inference engines.
• Experience with vector databases such as Qdrant, pgvector, or Pinecone, and with retrieval-augmented generation (RAG) architectures at scale.
• Ability to establish technical best practices, make architectural decisions, and mentor junior developers.

Preferred Qualifications:

• Experience building social media data pipelines involving platforms such as TikTok, Instagram Reels, or YouTube Shorts.
• Background in data-centric AI, active learning, or synthetic data generation.

Benefits:

• Fully remote working arrangement.
• Annual salary of $12,000–$16,000.
• Opportunity to work on advanced multimodal AI systems, open-source model deployment, and large-scale data intelligence.
• Technical leadership opportunities, including architectural planning and mentoring junior developers.