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

Accountabilities:

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.