1 month ago
Bellevue, WA, USAStaff+
Responsibilities
- Own end-to-end systems architecture across data pipelines, AI/ML platforms, semantic layers, and application interfaces.
- Build data ingestion, normalization, orchestration, API, and real-time streaming capabilities across structured and unstructured sources.
- Architect semantic intelligence capabilities including knowledge graphs, ontologies, vector embeddings, and RAG.
- Design and operate AI/ML platforms for training, deployment, and model lifecycle management.
- Integrate LLMs, embeddings, and multimodal models into production applications using MLOps tooling.
- Partner with product and design teams to deliver agentic and adaptive user experiences.
- Set platform architectural direction, document decisions, and define evolution strategies and layer boundaries.
- Drive fault tolerance, high availability, observability, and enterprise-scale performance.
- Mentor engineers on system design, coding standards, and operational excellence.
Requirements
- Bachelor’s or master’s degree in Computer Science, Engineering, or a related field.
- 10+ years of experience in systems architecture, software engineering, and platform development.
- Proven experience building scalable data platforms or AI-driven systems at enterprise scale.
- Deep programming expertise in Python, Java, or C++.
- Hands-on experience building distributed systems in cloud-native environments using Azure, AWS, or GCP, including multi-cloud.
- Fluency in real-time data processing, analytics frameworks, and big data technologies.
- Advanced knowledge of semantic modeling, knowledge graphs, ontology design, graph databases, graph embeddings, link prediction, and GNNs.
- Hands-on experience designing and deploying AI/ML pipelines with frameworks such as TensorFlow or PyTorch.
- Familiarity with microservices, event-driven architectures, and domain-driven design.
- Ability to set technical direction, communicate tradeoffs, and produce clear architecture decisions.
Tech Stack
Apache FlinkApache HadoopApache KafkaApache SparkAWSAzureC++DatabricksGoogle Cloud PlatformGraphQLgRPCJavaMLflowPythonPyTorchSnowflakeTensorFlow
Categories
Data EngineeringML Engineering
