About Us:Proofpoint is a global leader in humanHow We Work:At Proofpoint you’ll be part of a integral team that breaks barriers to redefine cybersecurity guided by our BRAVE core values:Bold in how we dream and innovateResponsive to feedback, challenges and opportunitiesAccountable for results and best in class outcomesVisionary in future focused problem-solvingExceptional in execution and impactThe Role:We are seeking an AI Knowledge Engineer to join our AI and Machine Learning organization at Proofpoint. This role is focused on enabling AI systems to operate across distributed enterprise knowledge ecosystems by creating semantic representations, governance frameworks, and knowledge architectures that unify information while preserving ownership, lineage, and trust.This is an early-career role intended for individuals with strong technical aptitude, curiosity, and a passion for solving complex information management challenges using modern AI technologies.What You Bring to the Team:As a core member of the AI organization, you will play a critical role in developing the knowledge infrastructure that powers enterprise AI initiatives. You will collaborate closely with data scientists, machine learning engineers, software engineers, product managers, and domain experts to transform organizational knowledge into scalable AI-ready assets.We are looking for someone with a strong foundation in semantic modeling, knowledge representation, data governance, and large language model technologies.



You should be passionate about enabling AI systems to reason effectively over enterprise knowledge and helping bridge the gap between organizational information and intelligent decision-making.Success in this role requires balancing technical implementation with information architecture principles, ensuring that knowledge remains accurate, discoverable, governed, and valuable across the organization.Success in This Role: Within your first 12 months, you will contribute to:- Building semantic knowledge models that connect previously isolated information sources.- Developing AI-ready knowledge architectures that improve agent accuracy and reasoning quality.- Establishing governance and metadata standards that improve trust and discoverability.- Supporting federated knowledge initiatives that allow AI systems to leverage knowledge while respecting ownership and compliance boundaries.- Measuring and improving how effectively AI agents discover, retrieve, reason over, and utilize enterprise knowledge.Day-to-Day Responsibilities:- Design and maintain semantic models that represent business concepts, relationships, and organizational knowledge.- Develop and manage knowledge representations that enable AI systems to reason over enterprise information.- Build and enhance knowledge graphs and related semantic technologies to connect information across disparate systems.- Create and maintain taxonomies, ontologies, metadata standards,



and governance frameworks.- Design and implement knowledge ingestion and extraction workflows that transform structured and unstructured content into AI-consumable assets.- Support the development of evaluation frameworks that measure the effectiveness, accuracy, and reliability of AI agents and knowledge-driven systems.- Design approaches for connecting and governing federated knowledge repositories without requiring centralization of all information.- Identify relationships, dependencies, and knowledge flows across systems, teams, products, and processes.- Develop mechanisms that improve the factual grounding, traceability, and explainability of AI-generated outputs.- Analyze organizational knowledge assets to uncover opportunities for reuse, automation, and business impact.- Work closely with stakeholders to understand information challenges and develop scalable knowledge solutions that support business objectives.- Continuously evaluate emerging technologies and methodologies in AI, knowledge engineering, semantic technologies, and enterprise information management.Knowledge Domains: The systems developed by this role may include:- Product knowledge- Security intelligence- Customer knowledge- Operational processes- Technical documentation- Organizational expertise- AI-generated knowledge assetsCore Principles:- AI systems are only as effective as the knowledge available to them.- Knowledge should be discoverable, governed, and explainable.- Semantic relationships are often more valuable than isolated data points.- Federated knowledge can create greater value than centralized repositories when properly connected.- Trust, lineage, and ownership are critical components of enterprise AI.Desired Skills & Experience:- Bachelor's degree in Computer Science, Information Science, Data Science, Artificial Intelligence, or a related field.- Strong Python programming skills.- Experience w

📌 Ai Knowledge Engineer (Córdoba)
🏢 Proofpoint
📍 Córdoba