OverviewMedallia is the pioneer and market leader in Experience Management.
Our award-winning SaaS platform, Medallia Experience Cloud, leads the market in the management of experiences, insights, and actions for candidates, customers, employees, patients, and residents alike.
We believe that every experience is a memory that can last a lifetime.
Experiences shape the way people feel about a company.
And they greatly influence how likely people are to advocate, contribute, and stay.
At Medallia, we are committed to creating a world where organizations are loved by their customers and their employees.We empower exceptional people to create extraordinary experiences together.
Bring your whole self.The RoleAt Medallia, we help the world's leading organizations understand and improve customer and employee experiences through real-time intelligence, analytics, and AI-driven action.
As enterprise AI evolves, context quality is becoming the defining factor in delivering trustworthy, personalized, and actionable AI experiences.We are building the next generation of enterprise AI systems where memory, grounding, semantic understanding, and organizational context become core infrastructure.
We are looking for a Principal Context & Knowledge Systems Engineer to define and build the knowledge architecture powering AI across Medallia.MissionSolve enterprise context engineering at scale.
This role is responsible for designing the foundational systems that enable AI to understand organizational knowledge, relationships, permissions, history, and real-time signals with high accuracy and relevance.
As AI systems increasingly depend on context quality rather than raw model capability, this role will lead the architecture for retrieval, memory, semantic understanding, and grounding across the enterprise.
You will build the intelligence layer that connects enterprise knowledge, user context, organizational structure, and live operational signals into reliable, permission-aware AI experiences.
ResponsibilitiesEnterprise Knowledge ArchitectureDesign and build enterprise-scale knowledge graph architectures representing organizational relationships, entities, workflows, and business contextDefine semantic models, ontologies, metadata standards, and taxonomy strategies across products and platformsBuild cross-system semantic federation capabilities connecting fragmented enterprise knowledge sourcesDevelop frameworks for knowledge normalization, enrichment, lineage,



and governanceRetrieval & Context SystemsArchitect and scale advanced Retrieval-Augmented Generation (RAG) infrastructureBuild hybrid vector and graph-based retrieval systems optimized for enterprise-scale knowledge discoveryDevelop intelligent context ranking, relevance scoring, compression, and summarization pipelinesImplement low-latency retrieval systems supporting real-time AI interactions and workflowsOptimize grounding accuracy, recall, precision, and contextual relevance across AI experiencesMemory & Identity-Aware IntelligenceBuild session, user, team, and organizational memory systems enabling persistent contextual intelligenceDesign identity-aware retrieval and permission-sensitive grounding architecturesDevelop personalized context systems that adapt to organizational roles, historical interactions, and business workflowsImplement secure context propagation and access-aware reasoning across distributed systemsReal-Time Context EnrichmentDesign streaming pipelines that enrich AI workflows with real-time operational signals and behavioral contextBuild event-driven enrichment systems integrating telemetry, workflows, and customer interaction dataDevelop mechanisms for continuous context updating, freshness management, and temporal reasoningCreate infrastructure enabling AI systems to dynamically adapt based on evolving enterprise statePlatform, Governance & Technical LeadershipEstablish architectural standards and best practices for enterprise knowledge systemsPartner with AI platform, infrastructure, product, and security teams to operationalize context intelligence at scaleDrive observability, evaluation, and quality metrics for retrieval and grounding systemsMentor engineers and influence long-term technical strategy across the organizationEvaluate emerging technologies in semantic retrieval, knowledge graphs, memory systems, and AI groundingCandidates based in the Buenos Aires vicinity will be prioritized as this role is Hybrid, 3 days per week onsite.
QualificationsMinimum Qualifications10+ years of experience building large-scale distributed systems, data platforms,



or search/retrieval infrastructure with expertise in information retrieval, semantic search, distributed data systems, or knowledge architecturesDemonstrated experience building or scaling RAG systems, vector search platforms, or contextual AI infrastructureDemonstrated experience with graph databases, vector databases, search indexing systems, or semantic retrieval technologiesDemonstrated understanding of embeddings, ranking systems, relevance optimization, and retrieval evaluationDemonstrated experience designing scalable metadata, ontology, or taxonomy systemsDemonstrated experience programming in Python, Java, Go, or similar backend technologiesDemonstreated experience with streaming systems, event-driven architectures, and real-time data pipelinesProven ability to lead highly complex technical initiatives across organizationsFluent in English, oral and writtenPreferred QualificationsExperience building enterprise knowledge graphs or semantic federation platformsFamiliarity with identity-aware access control and permission-sensitive retrieval systemsExperience with memory architectures for conversational or agentic AI systemsKnowledge of LLM grounding strategies, hallucination mitigation, and AI evaluation frameworksExperience working with unstructured enterprise data across SaaS platforms and operational systemsContributions to open-source retrieval, graph, or AI infrastructure ecosystemsWhat Success Looks LikeBuild a scalable enterprise context platform that dramatically improves AI accuracy, relevance, and trustworthinessEnable AI systems to reason effectively across organizational knowledge, relationships, and workflowsDeliver highly relevant, permission-aware retrieval experiences across Medallia products and internal platformsEstablish a unified semantic architecture connecting fragmented enterprise data ecosystemsImprove grounding quality, contextual awareness, and personalization across AI-powered experiencesCreate foundational infrastructure that accelerates the company's long-term AI strategyWhy Join MedalliaWork on one of the most important emerging problems in enterprise AI: context engineeringDefine the semantic and knowledge architecture powering next-generation AI systemsSolve deeply technical challenges involving retrieval, memory, grounding, and organizational intelligenceCollaborate with world-class engineers, architects, and AI leadersBuild foundational systems that will shape the future of enterprise software and AI experiences

📌 Principal Engineer, Context & Knowledge Systems (Buenos Aires)
🏢 Medallia
📍 Buenos Aires