Data Engineer - SMTS, LMTS, Knowledge Graph, AI

Responsibilities Build and scale Salesforce's Enterprise Knowledge Graph platform components, focusing on performance, data throughput, system reliability, high availability, and robust data integrity. Develop graph data models, write complex graph queries, and construct scalable data pipelines to ingest and map structured and unstructured data to enterprise ontologies and taxonomies. Write and maintain Python‑based semantic routing frameworks to parse, classify, and dynamically direct incoming queries to the appropriate knowledge graph indexes or vector databases. Build, integrate, and leverage AI‑powered developer tools and engineering automation platforms utilizing ecosystems such as Claude, Cursor, Windsurf, AI Agents, and Model Context Protocol (MCP) frameworks. Build scalable data pipelines and engineering patterns to ingest, transform, and orchestrate structured, unstructured, and third‑party data sources into graph‑based platforms mapped tightly to enterprise ontologies. Own the technical execution of specific platform features from concept through design, coding, testing, and production deployment. Participate heavily in code reviews, write comprehensive automated unit/integration tests, and ensure adherence to engineering standards and operational best practices. Provide technical guidance and mentorship to engineers on the team. Work closely with Lead/Principal Engineers, Product Managers, and Data Engineering teams to deliver robust features aligned with broader enterprise AI priorities. Requirements 8+ years of hands‑on software engineering experience in development, data engineering, distributed systems, or enterprise data platforms. A related technical degree required. Expert‑level coding skills in backend ecosystems, with strong fluency in Python and standard object‑oriented/functional programming languages. Hands‑on experience developing and deploying custom semantic routers using Python (leveraging native embeddings, LangChain, or mathematical logic like cosine similarity) alongside RAG architectures, vector search platforms, and AI workflows. Solid experience working with graph databases and semantic web concepts (e.g., Neo4j, RDF/OWL, SPARQL, property graphs) and mapping data to structured taxonomies. Practical experience configuring, testing, or integrating AI‑assisted engineering tools or automation workflows (e.g., Claude, Cursor, Windsurf, GitHub Copilot, or MCP frameworks). Proven experience building applications on cloud‑native systems (AWS, GCP, or Azure) utilizing microservices, REST/gRPC APIs, and event‑driven data streaming (e.g., Kafka). Track record of owning and successfully delivering complex features in an agile, production‑scale environment. #J-18808-Ljbffr Jobtailor