Data Engineer (SMTS/LMTS) - Knowledge Graph & AI

Salesforce•Indianapolis, IN

1d•$178,900 - $285,800•Hybrid

About The Position

Salesforce 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 serves as the foundational knowledge layer connecting enterprise data, business entities, ontologies, and relationships across multiple domains. We are seeking both a Senior Member of Technical Staff (SMTS) and a Lead Member of Technical Staff (LMTS) to join our Enterprise Knowledge Graph and AI Engineering team. The SMTS will serve as a senior engineer and core systems developer — heavily hands-on, developing, optimizing, and scaling core knowledge graph components, semantic pipeline workflows, and AI-powered frameworks. You will partner with Lead and Principal Engineers to implement technical designs and build production-ready scalable systems that support agentic AI use cases across the enterprise. The LMTS will serve as a hands-on technical lead, systems designer, and ontology engineer — designing, building, and scaling core knowledge graph infrastructure, semantic schemas, and AI-powered developer frameworks. You will partner closely with Principal Engineers, Product Management, Ontology experts, and Data Engineering teams to turn high-level engineering visions into production-ready scalable foundations. Both roles will actively implement and drive AI-powered engineering tools and developer platforms that improve engineering productivity, software quality, and delivery velocity across the organization.

Requirements

8+ years of hands-on software engineering experience in development, data engineering, distributed systems, or enterprise data platforms (for SMTS).
10+ years of hands-on experience in software engineering, data engineering, distributed systems, or enterprise data platforms (for LMTS).
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.
Solid, hands-on experience designing and building Knowledge Graph platforms, formal ontologies, semantic models, taxonomies, or enterprise metadata management systems (for LMTS).
Strong hands-on experience with graph technologies and ontology engineering tools (e.g., Neo4j, TopQuadrant, Protégé, RDF/OWL, SPARQL, SHACL, property graphs) and semantic reasoning frameworks (for LMTS).
Proven experience implementing graph-powered AI solutions, vector search platforms, Retrieval-Augmented Generation (RAG) architectures, and orchestrating agentic workflows (for LMTS).
Demonstrated hands-on experience designing, optimizing, and productionizing custom semantic routers using Python (leveraging native embeddings, LangChain, semantic-router, or specialized mathematical logic like cosine similarity) to decouple intent handling from expensive LLM calls (for LMTS).
Experience deploying and integrating AI-assisted engineering tools or automation workflows using ecosystems like Claude, Cursor, Windsurf, GitHub Copilot, or MCP frameworks (for LMTS).
Strong experience with cloud-native system designs (AWS, GCP, or Azure), distributed systems, microservices, and high-throughput event-driven systems (for LMTS).
Demonstrated experience leading feature teams, guiding technical execution, and mentoring mid-to-senior level engineers (for LMTS).

Nice To Haves

Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field (for SMTS).
Familiarity with ontology validation frameworks (e.g., SHACL) and data quality governance.
Experience building integrations with data platform environments like Salesforce Data Cloud or enterprise CRM metadata architectures.
Experience optimizing low-latency applications and heavy-throughput vector search lookups.
Passion for engineering automation and driving personal/team velocity via advanced AI development tools.
Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related technical field with a focus on Semantic Web or Knowledge Representation (for LMTS).
Direct experience integrating platforms with Salesforce Data Cloud, CRM platforms, or metadata-driven system designs.
Experience with semantic routing at enterprise scale, high-throughput enterprise search systems, and graph-powered recommendation engines.
Deep familiarity with advanced ontology governance, federated knowledge management, and data contract alignment.
Proven track record of optimizing engineering team velocity through the tailored implementation of AI developer tooling.

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.
Conduct deep-dive evaluations of emerging graph technologies, ontology modeling tools, semantic reasoning frameworks, vector databases, and AI tooling to continuously modernize the platform.