Lead AI Systems Architect, Data-Centric AI, Multimodal

• Design and build multimodal AI pipelines combining computer vision, speech-to-text (Whisper), and LLMs into automated ingestion workflows
• Implement programmatic extraction frameworks (Instructor, Pydantic, DSPy, Outlines) to enforce strict, validated JSON outputs from generative models
• Transition workloads from commercial APIs to fine-tuned open-source models (Llama 3/4, Qwen, DeepSeek) running on local inference engines (vLLM, TGI, Groq)
• Deploy data-centric AI frameworks (Cleanlab, Snorkel, Active Learning) to automate millions of labels and clean noisy datasets
• Guide junior developers, establish engineering best practices, and set architectural roadmaps for data enrichment
• Convert raw, unstructured social content (short-form video, audio, captions, and visual metadata) into high-precision structured data products

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

• Remote work arrangement