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.
  

Requirements

• 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.