Job Title: Senior AI Data Engineer (Data Platforms, GenAI, RAG, Cloud)
Location: Remote (Must be available to work PST hours)
Job Summary
We are seeking an experienced Senior AI Data Engineer to design, build, and optimize enterprise-scale data platforms that support Artificial Intelligence, Machine Learning, Generative AI, and advanced analytics initiatives. The ideal candidate will have extensive experience developing AI-ready data ecosystems, enabling scalable and governed data foundations for modern AI applications, large language models (LLMs), and agentic workflows.
This role requires expertise in cloud-native data architectures, data engineering best practices, AI/ML data pipelines, and modern data platform technologies.
Key Responsibilities
Design and develop scalable cloud-native data platforms supporting AI, Machine Learning, and advanced analytics workloads.
Build and maintain robust data ingestion, transformation, orchestration, and serving pipelines for structured and unstructured data.
Engineer AI-ready datasets that are curated, enriched, contextualized, and optimized for model training and inference.
Develop batch and real-time data processing solutions to support enterprise AI initiatives.
Enable semantic data layers, vectorized data architectures, and knowledge-driven data models.
Support AI/ML teams with feature engineering, model development, training pipelines, and deployment workflows.
Implement data governance, metadata management, lineage tracking, and data quality frameworks.
Optimize platform performance, scalability, reliability, and cost efficiency across cloud environments.
Design and maintain enterprise data architecture standards and engineering best practices.
Collaborate with data scientists, AI engineers, architects, and business stakeholders to deliver high-impact data solutions.
Provide technical leadership and mentor engineering teams on modern data platform strategies.
Required Qualifications
10+ years of experience in Data Engineering, Data Platform Architecture, or related disciplines.
Strong expertise designing and implementing modern cloud-based data platforms.
Hands-on experience with Azure, AWS, or Google Cloud Platform data services.
Experience supporting AI/ML workloads and data pipelines for model training and inference.
Strong understanding of data lake, data warehouse, and lakehouse architectures.
Experience building scalable ETL/ELT frameworks and real-time data processing pipelines.
Knowledge of data governance, metadata management, data lineage, and data security practices.
Strong SQL and data modeling expertise.
Experience with distributed data processing frameworks and large-scale datasets.
Excellent communication, collaboration, and problem-solving skills.
Preferred Qualifications
Experience supporting Generative AI, LLM, and Agentic AI initiatives.
Knowledge of Retrieval-Augmented Generation (RAG) architectures and semantic search solutions.
Experience with vector databases, embeddings, and AI knowledge retrieval systems.
Familiarity with data mesh and domain-driven data architecture principles.
Experience building enterprise-scale AI-ready data ecosystems.
Exposure to MLOps, Feature Stores, and AI model lifecycle management frameworks.
Experience supporting large-scale digital transformation and modernization initiatives.
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