Principal Software Engineer

Your core focus areas

• Platform architecture. Lead design and implementation of lakehouse table services and context graph ingestion. Translate product requirements into scalable, observable plans with clear failure-recovery patterns.
• LLMs and ontologies. Design how large language models and enterprise ontologies work together in the Context Engine: ontology-grounded retrieval and reasoning for LLMs, and LLM-assisted ontology construction, mapping, and evolution. Make both production-grade, observable, and governed.
• 1.0 to adoption. Drive the full arc from early release to meaningful adoption. Learn from how customers actually use the platform, not only what they say, and build feedback loops that improve adoption over time.
• AI-native engineering. Set the bar for using AI in engineering itself, from AI-assisted code review and test generation to platform features that treat AI agents as first-class users.
• Engineering leadership. Run deep architecture reviews, raise the technical bar across the team, and represent the platform in technical and leadership forums.
• Cross-functional execution. Work with Product, Design, and partner engineering teams to align dependencies and ship coherent experiences.

To Be Successful in This Role

• 10+ years in software engineering, including shipping complex enterprise-grade data or SaaS products at scale.
• Hands-on experience building products from zero, with ownership of both code and architecture decisions. Not prototypes.
• Deep cloud-native architecture: multi-tenant design, large-scale API design (REST/gRPC), Kubernetes, observability, and multi-cloud platforms.
• Distributed systems and event-driven architecture expertise, with a focus on reliability, fault tolerance, and operational observability.
• Strong data modeling across relational, graph, columnar, and streaming systems, including open table formats such as Iceberg and query engines such as Trino.
• Hands-on experience with data platform technologies: Spark for large-scale data processing, Kafka for event streaming, and Iceberg and Polaris for lakehouse table management and catalog services.
• Production-grade Python, with hands-on experience building services with FastAPI.
• Hands-on experience building LangChain or LangGraph applications, and MCP servers that expose platform capabilities to AI agents.
• Hands-on experience running LLMs in production: prompt and context design, structured outputs, model evaluation, and cost and latency trade-offs.
• Working knowledge of ontologies and semantic modeling (such as OWL, RDF, or SKOS), including how they constrain and ground LLM outputs.
• Proficiency across the full engineering lifecycle, using AI daily to improve delivery velocity and software quality.
• Technical influence without formal authority, with a record of driving architecture decisions across teams through clear reasoning.

Nice to Have

• Knowledge graph or context graph experience, including entity resolution and graph query optimization.
• Experience building ontologies or semantic layers at enterprise scale, including ontology alignment and schema mapping across data sources.
• Experience fine-tuning or evaluating LLMs for entity extraction, classification, or relation extraction.
• Data governance, lineage, or catalog product experience, including auditability and access control.
• Production experience with data pipeline ingestion from SaaS sources.
• Familiarity with OpenAPI and other AI interoperability standards.
• Advanced degree in Computer Science, AI, or a related field.

Additional Information: Work Personas

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