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
#LI-CP2
