Elan is building a platform for developers to build, run, and monetize specialized AI agents. Open Context Engine (OCE) provides the business meaning and governed context those agents depend on.
As Lead Semantic AI Engineer, you will own the architecture and hands-on engineering of OCE. You will turn semantic models, enterprise data, policies, and changing business facts into production services that agents can use reliably.
What you will own
Semantic foundations. Design and implement ontologies, canonical models, business vocabularies, metrics, policies, and knowledge graphs. Use semantic standards where they serve the product, with clear validation and versioning.
Context services. Build the APIs and services that assemble relevant, authorized, evidence-backed context for agents across structured data, documents, and graph sources.
Temporal reasoning. Model when facts are valid and when they became known. Handle historical queries, late updates, corrections, and versioned knowledge so agents receive context appropriate to the decision.
Validation and reasoning. Implement schema and constraint checks, consistency checks, and reasoning workflows, including RDF/OWL, SPARQL, and SHACL where appropriate.
Context evaluation. Work with the Applied AI Engineer to measure relevance, completeness, authority, temporal validity, consistency, authorization, evidence sufficiency, traceability, and outcome quality. Demonstrate how context improvements affect agent results.
Production engineering. Write and review Python services, APIs, tests, and data pipelines. Own performance, tenant isolation, release compatibility, observability, and operational reliability with platform and infrastructure engineers.
Technical direction. Evaluate open-source components, make architecture decisions, document tradeoffs, and guide implementation while remaining a hands-on contributor.
Minimum requirements
8 to 15 years of software, knowledge, data, or platform engineering experience, with substantial hands-on ownership of semantic systems.
Strong production Python and API engineering. You can independently build, debug, test, and maintain complex services rather than only prototype or configure tools.
Practical experience building ontologies, knowledge graphs, or semantic platforms using RDF, OWL, SPARQL, SHACL, or closely related technologies.
Experience translating domain knowledge into formal models, mapping models to real data, and managing changes without breaking downstream consumers.
Recent hands-on experience connecting semantic or knowledge systems to LLM applications or AI agents.
Strong SQL and data modeling, with experience integrating relational, graph, and document sources.
Ability to design temporal and versioned data models, explain their tradeoffs, and reason about corrections and historical validity.
Experience evaluating system correctness with explicit test cases, measurable baselines, regression checks, and traceable evidence.
Ownership of a production system through deployment, performance issues, and incidents, plus clear technical communication and architecture judgment.
Good to have
Experience with Neo4j, Neptune, GraphDB, Stardog, or other graph and semantic infrastructure.
Experience with ontology induction, entity resolution, query planning, federated queries, or context compilation.
Work on bitemporal systems, policy enforcement, governed metrics, or semantic and context evaluation frameworks.
Open-source contributions or technical leadership in semantic technologies, AI infrastructure, or developer platforms.
Experience working directly with domain experts in retail, hospitality, finance, or other enterprise industries.
How you will work
This role can be based in the United States or in Noida, India. The Noida position follows a hybrid working arrangement.
Work closely with Elan founders, product, Applied AI, platform engineering, and domain experts. Lead architecture and implementation while building and shipping code yourself.
United States annual salary range: US$150,000–$240,000. Compensation for the Noida position will be discussed during the hiring process.
What to share with your application
A project, repository, or technical write-up describing a semantic or knowledge system you personally built. Explain what you coded, the production tradeoffs you owned, and how you tested correctness. Do not share confidential employer or customer information.
