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

This is a remote position.

• Design & Build Multimodal AI Pipelines: Combine Computer Vision (scene classification aesthetic scoring) Speech-to-Text (Whisper) and LLMs into automated ingestion workflows.
  
• Structured Data Extraction: Implement programmatic extraction frameworks (Instructor Pydantic DSPy Outlines) to enforce strict validated JSON outputs from generative models.
  
• Model Optimization & Cost Control: Transition workloads from commercial APIs to fine-tuned open-source models (Llama 3/4 Qwen DeepSeek) running on local inference engines (vLLM TGI Groq).
  
• Programmatic Labeling & Quality Control: Deploy data-centric AI frameworks (Cleanlab Snorkel Active Learning) to automate millions of labels and clean noisy datasets.
  
• Technical Leadership: Guide junior developers establish engineering best practices and set architectural roadmaps for data enrichment.
  

• 5 years of experience in Applied AI Natural Language Processing or Computer Vision.
  
• Proven experience building multimodal pipelines integrating vision speech and text models.
  
• Mastery of Python PyTorch/TensorFlow and production LLM orchestration tools (DSPy Instructor Pydantic LangChain/LlamaIndex).
  
• Hands-on experience fine-tuning and serving open-source models using vLLM TGI or similar high-throughput inference engines.
  
• Experience with vector databases (Qdrant pgvector Pinecone) and RAG architectures at scale.
  

• Prior experience with social media data pipelines (TikTok Instagram Reels YouTube Shorts).
  
• Background in data-centric AI active learning or synthetic data generation.