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Job Category
Software Engineering
Job Details
The ExperienceSalesforce is building the next generation Enterprise Knowledge Graph platform to power AI-driven experiences, agentic applications, semantic search, enterprise data discovery, and intelligent decision-making across the company. The platform will serve as the foundational knowledge layer connecting enterprise data, business entities, ontologies, and relationships across multiple domains.
We are seeking a Principal Member of Technical Staff (PMTS) to provide technical leadership and architectural direction for Salesforce's Enterprise Knowledge Graph strategy. This role will be responsible for defining the long-term vision, architecture, and execution strategy for Knowledge Graph platforms, semantic technologies, ontology-driven systems, graph data engineering, and AI-powered developer productivity solutions. The PMTS will partner closely with Architecture, Product Management, Ontology, Data Engineering, and AI Platform teams to establish a scalable foundation that supports current and future agentic AI use cases across the enterprise.
In addition to Knowledge Graph leadership, this role will drive the strategy and productionization of AI-powered engineering tools and developer platforms that improve engineering productivity, software quality, operational efficiency, and delivery velocity. The ideal candidate combines deep expertise in Knowledge Graph technologies with a proven track record of leading large-scale technical initiatives and successfully bringing AI-powered engineering solutions from concept to production.
What You'll Actually Be Doing- 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 productionizing 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.
You're Our Person If...- 12+ years of experience in software engineering, data engineering, distributed systems, enterprise data platforms, or related technical domains.
- A related technical degree 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, TopQuadrant, 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 productionizing AI-powered developer tools, engineering platforms, or automation solutions using technologies such as Claude, Cursor, Windsurf, GitHub 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 engineering practices.
- Demonstrated ability to influence senior technical leaders, executives, architects, and cross-functional stakeholders.
- Proven track record of defining technical strategy and driving execution across multiple teams and organizations.
- Excellent communication, leadership, and stakeholder management skills.
Even Better If...- Master's degree or PhD in Computer Science, Artificial Intelligence, Data Science, Information Systems, or a related field.
- Experience building enterprise Knowledge Graph platforms supporting large-scale AI and agentic ecosystems.
- Experience with Salesforce Data Cloud, CRM platforms, metadata-driven architectures, or enterprise data platforms.
- Experience with semantic routing, enterprise search, graph-powered recommendation systems, and intelligent retrieval architectures.
- Experience with vector databases, Retrieval-Augmented Generation (RAG), AI agents, MCP frameworks, and emerging AI infrastructure technologies.
- Experience leading enterprise-wide platform initiatives spanning multiple organizations and business domains.
- Strong understanding of ontology governance, federated knowledge management, and enterprise semantic architecture.
- Demonstrated track record of driving measurable improvements in engineering productivity through AI-powered tooling and automation.
- Publications, patents, conference presentations, or recognized industry leadership in Knowledge Graphs, Semantic Technologies, AI Engineering, or related domains.
