| Location | Austin, Texas |
Virtasant is a global technology services company with a network of over 4,000 technology professionals across 130+ countries. We specialize in cloud architecture, infrastructure, migration, and optimization, helping enterprises scale efficiently while maintaining cost control.
Our clients range from Fortune 500 companies to fast-growing startups, relying on us to build high-performance infrastructure, optimize cloud environments, and enable continuous delivery at scale.
We are building a senior team for a large enterprise program in healthcare operations. The program uses ontology and knowledge-graph design to help executives make operational decisions, act on them, and measure the results.
We need a senior ontology lead who has done this before and can show it. You will work directly with senior client stakeholders, share real experience of ontologies you have designed and delivered, and then lead the ontology and semantic design.
*Immediate start, part-time for the first few weeks.
*Expected to move to full time on a multi-year engagement.
*Contractor. US-based with US work authorization. Occasional US travel
*You have designed and shipped production ontologiesthat drove real decisions, not just taxonomies or metadata catalogs.
*You can talk through two or three of them: the business problem, the model, what went wrong and what you would do differently.
*Business-first modeling. You start from the questions executives ask and the metrics they run on, run workshops with domain experts, and only then turn that into a technical model.
*Hands-on graph modeling with a property-graph or RDF database. You know when a graph earns its place and when it doesn't, and you can make that case to a skeptical architect.
*Healthcare operations experience, for example, workforce, scheduling, or clinical operations.
*Senior presence. You have presented to C-level stakeholders and are comfortable being challenged live.
*Available immediately.
*Palantir Foundry Ontology (objects, links, actions).
*Ontology governance: versioning, change control, stable identifiers, a shared core with per-client extensions.
*Decision-intelligence modeling: KPI trees, root-cause analysis, recommendations, and closed-loop measurement of predicted against actual outcomes.
*Grounding AI assistants or agents in an ontology or knowledge graph.
*Rules engines alongside an ontology.
*Modern cloud data platforms.
*Mainly academic semantic-web work with no production delivery.
*Data engineers or BI developers who have worked near an ontology but never owned one