AI Knowledge Engineer

About Us

Proofpoint is a global leader in human- and agent-centric cybersecurity.

We protect how people, data, and AI agents connect across email, cloud, and collaboration tools.

Over 80 of the Fortune 100, 10,000 large enterprises, and millions of smaller organizations trust Proofpoint to stop threats, prevent data loss, and build resilience across their people and AI workflows.

Our mission is simple: safeguard the digital world and empower people to work securely and confidently.

Join us in our pursuit to defend data and protect people.

How We Work

At Proofpoint you’ll be part of a global team that breaks barriers to redefine cybersecurity guided by our BRAVE core values:

• Bold in how we dream and innovate
• Responsive to feedback, challenges and opportunities
• Accountable for results and best in class outcomes
• Visionary in future focused problem-solving
• Exceptional in execution and impact

The 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.

As an AI Knowledge Engineer, you will help design and build semantic knowledge systems that enable AI agents, retrieval systems, and decision-support applications to reason over complex organizational information.

You will work at the intersection of knowledge representation, semantic modeling, governance, and generative AI, helping establish the foundations that allow AI systems to operate effectively across federated and distributed knowledge sources.

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.
• Collaborate with AI and machine learning teams to integrate knowledge systems with LLM-powered applications, retrieval systems, and agentic workflows.
• 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
• Product knowledge
• Security intelligence
• Customer knowledge
• Operational processes
• Technical documentation
• Organizational expertise
• AI-generated knowledge assets
• Core 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 working with Large Language Models (LLMs) and modern generative AI platforms.
• Strong understanding of semantic modeling and knowledge representation.
• Deep appreciation for data governance and information lifecycle management.
• Familiarity with ontology development, taxonomy management, and metadata design.
• Experience with knowledge graphs, graph databases, and graph query languages is preferred.
• Understanding of retrieval-augmented generation (RAG) architectures and knowledge-grounded AI systems.
• Experience processing structured and unstructured information.
• Exposure to agentic AI systems and AI evaluation methodologies.
• Familiarity with modern AI orchestration frameworks such as Lang Graph, Lang Chain, Llama Index, MCP, or similar platforms is a plus.
• Basic understanding of cybersecurity concepts and enterprise security practices is desirable.

Why Proofpoint?

• Competitive compensation
• Comprehensive benefits
• Career success on your terms
• Flexible work environment
• Annual wellness and community outreach days
• Always on recognition for your contributions
• Global collaboration and networking opportunities

Our culture is rooted in values that inspire belonging, empower purpose and drive success-every day, for everyone.

We encourage applications from individuals of all backgrounds, experiences, and perspectives.

If you need accommodation during the application or interview process, please reach out to accessibility@proofpoint. com.

How to Apply

Interested? Submit your application along with any supporting information- we can’t wait to hear from you!

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