Sr.AIDataEngineer

## Accountabilities Design and maintain large-scale, AI-augmented data pipelines that combine SQL transformations with ML model invocations for data cleaning, labeling, and enrichment. Own end-to-end remote inference orchestration, including batching, asynchronous execution, retry logic, failure handling, and performance optimization. Build and manage scalable embedding pipelines, including vector generation, storage, indexing, and similarity search infrastructure. Curate and govern large-scale training datasets for image generation models using model-driven signals such as classifiers, aesthetic scoring, and content filters. Develop automated annotation systems using LLMs and vision models, including evaluation frameworks to measure annotation quality and model performance. Contribute to shared engineering frameworks and reusable tooling for AI-driven data workflows and pipeline orchestration. Ensure pipeline reliability, compliance, and data quality across billions of records in distributed production systems. Collaborate with ML researchers and engineers to improve dataset quality, evaluation metrics, and generative model performance. Requirements: Bachelor’s degree or higher in Computer Science, Data Engineering, Machine Learning, or a related STEM field. 5+ years of experience in data engineering, ML engineering, or hybrid roles involving data pipelines and model inference systems. Strong expertise in SQL, data pipeline orchestration tools (e.g., Airflow, Dataswarm), and large-scale distributed systems. Hands-on experience integrating ML models into production pipelines, including inference APIs, batching, and failure handling. Experience with AI-assisted development tools (e.g., Copilot, Cursor, Codex) to accelerate engineering workflows. Strong programming and debugging skills with a focus on scalable data systems and production reliability. Experience with embeddings, vector databases, or similarity search systems (e.g., FAISS, Milvus) is highly desirable. F