Lead AI Systems Architect (Data-Centric AI & Multimodal)
# Lead AI Systems Architect (Data-Centric AI & Multimodal) **AWISEE** · United States · `Remote` · `Full Time` 💼 **Уровень роли:** `Senior` 💰 **Компенсация:** `$115,000 – $160,000` 🕒 **Статус:** *Опубликовано: сегодня* · *Источник: Himalayas (JSON API)* --- ### Top Skills & Match 🎯 **Ключевой стек роли:** `[AI-Engineer]` `[Machine-Learning-Engineer]` `[AI-Architect]` `[AI-Systems-Architect]` `[Generative-AI-Engineer]` `[Senior-AI-Architect]` `[Principal-AI-Systems-Engineer]` `[Principal-AI-Solutions-Architect]` `[Data-and-AI-Solution-Architect]` `[AI-Data-Infrastructure-Lead]` --- ### About the Role This is a remote position. We are building a an AI & Data Lab focused on social media intelligence and influencer marketing data. We are seeking a Lead AI Systems Architect to design and scale our automated classification, enrichment, and multimodal AI pipelines. You will take raw, unstructured social content (short-form video, audio, captions, and visual metadata) and convert it into high-precision structured data products. Responsibilities - 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. Preferred Qualifications - Prior experience with social media data pipelines (TikTok, Instagram Reels, YouTube Shorts). - Background in data-centric AI, active learning, or synthetic data generation. Originally posted on Himalayas
- AI-Engineer
- Machine-Learning-Engineer
- AI-Architect
- AI-Systems-Architect
- Generative-AI-Engineer
- Senior-AI-Architect
- Principal-AI-Systems-Engineer
- Principal-AI-Solutions-Architect
- Data-and-AI-Solution-Architect
- AI-Data-Infrastructure-Lead
Наблюдалась 2026-10-11, впервые 2026-10-11, источник — Himalayas (JSON API).