Principal Engineer
Job in
Seattle, King County, Washington, 98127, USA
Listed on 2026-07-09
Listing for:
salesforce.com, inc.
Full Time
position Listed on 2026-07-09
Job specializations:
-
Software Development
AI Engineer (Applied/Software), Software Architect, Cloud Engineer - Software
Job Description & How to Apply Below
Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn't a buzzword – it's a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.
Ready to level‑up your career at the company leading workforce transformation in the agentic era? You are in the right place! Agentforce is the future of AI, and you are the future of Salesforce.
Responsibilities- Define and drive the long‑term technical vision, architecture, and roadmap for Salesforce's Enterprise Knowledge Graph platform.
- Lead architecture and design for knowledge graph ecosystems, including graph data models, ontologies, semantic layers, entity resolution frameworks, graph APIs, vector search capabilities, and retrieval architectures supporting AI and agentic use cases.
- Establish enterprise standards, governance models, engineering patterns, and best practices for Knowledge Graph development, deployment, and lifecycle management.
- Define strategies for integrating structured, unstructured, and third‑party data sources into graph‑based platforms using scalable data engineering patterns.
- Partner with Architecture, Product, AI Platform, and Data Engineering organizations to align platform investments with enterprise priorities and future AI initiatives.
- Drive technical direction for semantic routing, graph‑powered retrieval, enterprise search, agent orchestration, and federated knowledge access patterns.
- Lead evaluation, selection, and adoption of graph technologies, semantic platforms, vector databases, and AI infrastructure required to support enterprise‑scale workloads.
- Define and drive the strategy for AI‑powered developer tooling, engineering automation, and productivity platforms that leverage technologies such as Claude, Cursor, Windsurf, AI Agents, MCP frameworks, and related AI ecosystems.
- Lead teams in product ionizing AI‑enabled engineering solutions, ensuring scalability, security, governance, reliability, and measurable productivity improvements.
- Provide technical leadership and architectural guidance across PMTS, LMTS, SMTS, and contractor teams while driving alignment across multiple organizations.
- Serve as the primary technical authority for complex architectural decisions, platform investments, and long‑term engineering strategy.
- Foster innovation and continuous improvement while establishing a culture of engineering excellence, technical rigor, and operational maturity.
- 12+ years of experience in software engineering, data engineering, distributed systems, enterprise data platforms, or related technical domains.
- A related technical degree is required.
- Proven experience defining and delivering enterprise‑scale Knowledge Graph platforms supporting AI, semantic search, data integration, and agentic applications.
- Deep expertise in Knowledge Graph technologies, ontology engineering, semantic modeling, linked data, graph databases, and enterprise metadata management.
- Strong hands‑on experience with graph technologies such as Neo4j, Top Quadrant, RDF/OWL, SPARQL, property graph models, semantic reasoning frameworks, or similar technologies.
- Proven experience leading the architecture and implementation of graph‑powered AI solutions, semantic retrieval systems, vector search platforms, RAG architectures, and agentic workflows.
- Demonstrated success in building, scaling, and product ionizing AI‑powered developer tools, engineering platforms, or automation solutions using technologies such as Claude, Cursor, Windsurf, Git Hub Copilot, AI agents, MCP frameworks, or similar ecosystems.
- Strong experience designing enterprise data engineering architectures, including large‑scale ingestion, transformation, orchestration, metadata management, and data governance frameworks.
- Experience with cloud‑native architectures and platforms including AWS, GCP, or Azure.
- Strong understanding of distributed systems, APIs, microservices, event‑driven architectures, and modern software…
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