Data Engineer – AI

🌍 Remote, USA 🎯 Full-time 🕐 Posted Recently

Job Description

Define and drive the technical vision for data platforms that support AI-powered features in Crossplane and Upbound Spaces
Lead the design of data pipelines that transform infrastructure and data into training datasets for ML models
Architect vector search and RAG systems that leverage Crossplane Control Planes & Upbound Marketplace as a knowledge store
Build data infrastructure that processes resources, extensions, and compositions for semantic search
Establish frameworks for collecting, processing, and analyzing infrastructure configuration data
Design data pipelines that handle Crossplane-specific data
Create infrastructure for indexing and searching Upbound Marketplace content, documentation, and community patterns
Develop metrics and monitoring for AI features integrated with Upbound's control plane architecture
Design data systems that power AI agents for infrastructure provisioning & operations, helping users generate and optimize Crossplane compositions
Create feature engineering platforms that extract signals from control plane operations, resource status, and reconciliation patterns
Implement data infrastructure for training models that predict infrastructure failures, optimize resource allocation, and suggest configuration improvements
Drive the development of knowledge graph representations of infrastructure dependencies and relationships

10+ years of software/data engineering experience with at least 4 years in technical leadership roles
Proven track record building data platforms that support production systems at scale
Deep expertise in both traditional data engineering (Spark, Airflow, data lakes) and ML-specific infrastructure (feature stores, model serving)
Experience with vector databases (Pinecone, Weaviate, Qdrant, Milvus, pgvector, Opensearch, ElasticSearch)
Demonstrated experience with LLM applications, including RAG architectures and semantic search implementations
Understanding of Kubernetes, cloud-native architectures, and infrastructure-as-code principles
Strong understanding of data requirements for AI/ML systems: training pipelines, feature stores, and inference infrastructure
Hands-on experience building knowledge bases and semantic search systems for technical documentation and code
Experience with embedding models for code and technical documentation
Knowledge of time-series data processing for infrastructure metrics and events
Understanding of graph databases and their application to infrastructure dependency modeling