Principal Engineer, Semantic Platforms and Graph Intelligence

Our company

At Teradata, we believe that people thrive when empowered with better information. Teradata Autonomous Knowledge Platform activates enterprise intelligence by unifying data, knowledge and business context to achieve tangible outcomes. With Teradata, organizations can provide agents with full context for impact when it matters. Our solution lets businesses connect and scale on premises, in the cloud, or through a hybrid approach. Teradata delivers real business value with AI.

What You’ll Do

We are seeking a world-class Principal Knowledge Graph Engineer to lead the architecture, development, and evolution of next-generation knowledge graph platforms that power AI, reasoning, discovery, and autonomous decision-making at enterprise scale for Teradata Context Engine.

This role is intended for a recognized technical leader who has built and operated very large-scale knowledge graphs comprising billions of entities, relationships, events, documents, and embeddings derived from both structured and unstructured data. You will own, and drive innovation at the intersection of graph technologies, semantic modeling, information retrieval, machine learning, agents, and generative AI for Teradata Context Engine, our Autonomous Knowledge Platform. Context Engine is a platform that converts enterprise data estates into governed, agent-ready knowledge. Built for organizations where AI reliability and auditability are non-negotiable, Context Engine enables companies to deploy AI workflows on the data they already own — without rebuilding their infrastructure, without retraining their models, and without waiting for generic AI synthesis to learn the business terminology that took their analysts years to master.

Technical Leadership

• Define and execute the long-term vision for enterprise knowledge graph architecture and graph-enabled AI systems.
• Lead the design of highly scalable graph platforms supporting billions of nodes and edges across multiple data domains.
• Establish standards, frameworks, and best practices for graph modeling, ontology design, semantic interoperability, and graph lifecycle management.
• Serve as the company's senior technical authority on graph technologies, semantic systems, and knowledge representation.

Knowledge Graph Architecture

• Design and implement large-scale knowledge graphs that integrate:
• Structured enterprise data
• Semi-structured data
• Documents and content repositories
• Event streams
• APIs and external data sources
• Embedding and vector representations
• LLM-generated knowledge artifacts
• Develop scalable entity resolution, identity graph, and record-linkage frameworks.
• Build semantic layers that unify business concepts across disparate systems.

AI, Retrieval & Reasoning

• Design hybrid retrieval architectures combining:
• Knowledge graphs
• Vector databases
• Full-text search
• Semantic retrieval
• Agentic workflows
• Enable GraphRAG and graph-enhanced AI experiences.
• Develop graph-based reasoning systems that support explainable AI and autonomous decision-making.
• Apply graph algorithms, network analysis, and graph machine learning to generate insights and predictions.

Engineering & Platform Development

• Build distributed graph ingestion and processing pipelines for large-scale enterprise environments.
• Optimize graph performance, storage, indexing, partitioning, caching, and query execution.
• Implement graph observability, quality assurance, lineage, governance, and monitoring capabilities.
• Contribute production-grade code and lead the development of reusable graph services and platforms.

Industry Leadership

• Stay at the forefront of advancements in:
• Knowledge graphs
• Semantic technologies
• Graph databases
• Graph machine learning
• Retrieval systems
• AI agents
• Large and small language models
• Publish technical papers, patents, blogs, conference talks, and thought leadership where appropriate.
• Mentor senior engineers and influence architecture across the organization.
• A plus would be in-depth knowledge in at least one vertical, such as Healthcare, Finance, etc.

Who You’ll Work With

You will work hands-on across the full lifecycle of graph systems, from ontology engineering and graph construction to retrieval, reasoning, embeddings, graph machine learning, and AI-powered applications. This is not a management role. We are looking for a builder, architect, and technical thought leader who can design systems, write code, solve hard problems, and influence strategy.

What Makes You a Qualified Candidate

Experience

• 12+ years of software engineering experience.
• 8+ years designing and implementing enterprise-scale graph systems.
• Proven experience building and operating knowledge graphs with hundreds of millions to billions of nodes and relationships.
• Demonstrated success integrating structured, semi-structured, and unstructured data into production graph platforms.
• Experience leading architecture for mission-critical data or AI platforms.

Deep Knowledge of Graph Technologies

Expert-level understanding of:

• Property graphs
• RDF, RDFS, OWL
• Knowledge representation and reasoning
• Ontology design and semantic modeling
• Taxonomy development
• Entity resolution and identity graphs
• Graph query languages:
• Cypher
• SPARQL
• Gremlin
• GraphQL

Graph Platforms

Strong experience with several of:

• JanusGraph
• Neo4j
• Amazon Neptune
• TigerGraph
• Memgraph
• ArangoDB
• Stardog
• Ontotext GraphDB
• Virtuoso

AI and Machine Learning

Hands-on experience with:

• GraphRAG architectures
• Retrieval-augmented generation
• Graph embeddings
• Knowledge-enhanced LLM systems
• Vector databases
• Graph neural networks
• Link prediction
• Node classification
• Recommendation and ranking systems

Large-Scale Data Engineering

Expertise in:

• Distributed systems
• Streaming architectures
• Spark
• Kafka
• Data pipelines
• Data governance
• Data quality
• Metadata management
• Cloud-native architectures

Software Engineering

Strong coding ability in:

• Python
• Java
• Scala
• Go
• C#

Experience with:

• API design
• Distributed computing
• Containerization
• Kubernetes
• CI/CD
• Cloud-native development

What You’ll Bring

• PhD or MS in Computer Science, Information Science, Knowledge Representation, Artificial Intelligence, Machine Learning, or related disciplines.
• Contributions to widely adopted graph technologies or open-source projects.
• Published research in knowledge graphs, semantic technologies, graph machine learning, information retrieval, or AI.
• Experience building enterprise GraphRAG platforms at production scale.
• Experience developing graph-powered autonomous agents and AI systems.
• Experience deploying semantic systems in air-gapped, on-prem and regulated environments.
• Experience developing agent harnesses on open-source (e.g., strands, LangChain), Azure, AWS and GCP platforms.
• Recognized industry expert, conference speaker, or technical thought leader.

Characteristics of an exceptional candidate

The ideal candidate has personally:

• Built knowledge graphs containing billions of entities and relationships.
• Designed ontologies spanning multiple enterprise domains.
• Solved large-scale entity resolution and identity graph challenges.
• Integrated graph, vector, and search technologies into unified retrieval platforms.
• Leveraged knowledge graphs to improve AI reasoning, explainability, and decision-making.
• Led architecture decisions that influenced organizations, products, and platforms at industry scale.
• Remained deeply hands-on despite operating at a principal or distinguished engineer level.

Success metrics

Within the first 12 months, you will:

• Establish a scalable enterprise knowledge graph architecture.
• Accelerate GraphRAG and AI capabilities through graph-enhanced retrieval and reasoning.
• Improve knowledge discovery, relationship analysis, and semantic interoperability across the enterprise.
• Enable graph-powered applications that drive measurable business and operational outcomes.
• Raise the technical bar for graph engineering, semantic technologies, and AI platform architecture across the organization.

Why We Think You'll Love Teradata 

We prioritize a people-first culture because we know our people are at the very heart of our success. We embrace a flexible work model because we trust our people to make decisions about how, when, and where they work. We focus on well-being because we care about our people and their ability to thrive both personally and professionally. We are committed to actively working to foster an inclusive environment that celebrates people for all of who they are

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