Data & Analytics Jobs in Buenos Aires, Argentina (Now Hiring) — 354 open — Page 15

Overview

Medallia 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 Role

At 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.

 

Mission

Solve 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.

 

Responsibilities

Enterprise Knowledge Architecture

• Design and build enterprise-scale knowledge graph architectures representing organizational relationships, entities, workflows, and business context
• Define semantic models, ontologies, metadata standards, and taxonomy strategies across products and platforms
• Build cross-system semantic federation capabilities connecting fragmented enterprise knowledge sources
• Develop frameworks for knowledge normalization, enrichment, lineage, and governance

Retrieval & Context Systems

• Architect and scale advanced Retrieval-Augmented Generation (RAG) infrastructure
• Build hybrid vector and graph-based retrieval systems optimized for enterprise-scale knowledge discovery
• Develop intelligent context ranking, relevance scoring, compression, and summarization pipelines
• Implement low-latency retrieval systems supporting real-time AI interactions and workflows
• Optimize grounding accuracy, recall, precision, and contextual relevance across AI experiences

Memory & Identity-Aware Intelligence

• Build session, user, team, and organizational memory systems enabling persistent contextual intelligence
• Design identity-aware retrieval and permission-sensitive grounding architectures
• Develop personalized context systems that adapt to organizational roles, historical interactions, and business workflows
• Implement secure context propagation and access-aware reasoning across distributed systems

Real-Time Context Enrichment

• Design streaming pipelines that enrich AI workflows with real-time operational signals and behavioral context
• Build event-driven enrichment systems integrating telemetry, workflows, and customer interaction data
• Develop mechanisms for continuous context updating, freshness management, and temporal reasoning
• Create infrastructure enabling AI systems to dynamically adapt based on evolving enterprise state

Platform, Governance & Technical Leadership

• Establish architectural standards and best practices for enterprise knowledge systems
• Partner with AI platform, infrastructure, product, and security teams to operationalize context intelligence at scale
• Drive observability, evaluation, and quality metrics for retrieval and grounding systems
• Mentor engineers and influence long-term technical strategy across the organization
• Evaluate emerging technologies in semantic retrieval, knowledge graphs, memory systems, and AI grounding

 

Candidates based in the Buenos Aires vicinity will be prioritized as this role is Hybrid, 3 days per week onsite.

Qualifications

Minimum Qualifications

• 10+ 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 architectures
• Demonstrated experience building or scaling RAG systems, vector search platforms, or contextual AI infrastructure
• Demonstrated experience with graph databases, vector databases, search indexing systems, or semantic retrieval technologies
• Demonstrated understanding of embeddings, ranking systems, relevance optimization, and retrieval evaluation
• Demonstrated experience designing scalable metadata, ontology, or taxonomy systems
• Demonstrated experience programming in Python, Java, Go, or similar backend technologies
• Demonstreated experience with streaming systems, event-driven architectures, and real-time data pipelines
• Proven ability to lead highly complex technical initiatives across organizations
• Fluent in English, oral and written

Preferred Qualifications

• Experience building enterprise knowledge graphs or semantic federation platforms
• Familiarity with identity-aware access control and permission-sensitive retrieval systems
• Experience with memory architectures for conversational or agentic AI systems
• Knowledge of LLM grounding strategies, hallucination mitigation, and AI evaluation frameworks
• Experience working with unstructured enterprise data across SaaS platforms and operational systems
• Contributions to open-source retrieval, graph, or AI infrastructure ecosystems

What Success Looks Like

• Build a scalable enterprise context platform that dramatically improves AI accuracy, relevance, and trustworthiness
• Enable AI systems to reason effectively across organizational knowledge, relationships, and workflows
• Deliver highly relevant, permission-aware retrieval experiences across Medallia products and internal platforms
• Establish a unified semantic architecture connecting fragmented enterprise data ecosystems
• Improve grounding quality, contextual awareness, and personalization across AI-powered experiences
• Create foundational infrastructure that accelerates the company’s long-term AI strategy

Why Join Medallia

• Work on one of the most important emerging problems in enterprise AI: context engineering
• Define the semantic and knowledge architecture powering next-generation AI systems
• Solve deeply technical challenges involving retrieval, memory, grounding, and organizational intelligence
• Collaborate with world-class engineers, architects, and AI leaders
• Build foundational systems that will shape the future of enterprise software and AI experiences

 

At Medallia, we celebrate diversity and recognize the value it brings to our customers and employees. Medallia is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age (40 and over), disability, genetic information, veteran status or military service, or any other status protected by state or local law. Individuals with a disability who need an accommodation to apply please contact us at [email protected]. For information regarding how Medallia collects and uses personal information, please review our Privacy Policies. Applications will be accepted for 30 days from the date this role was posted or until the role has been filled.