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Knowledge Engineering Jobs in Oregon (NOW HIRING)

Senior Applied AI Engineer

OR · Remote

$122K - $161K/yr

Experience with prompt engineering, embeddings, semantic search, vector databases, and knowledge retrieval. * Experience evaluating AI model performance and implementing experimentation frameworks.

OR · On-site

$121K - $156K/yr

Act as the primary Knowledge representative for governance-related work with Product, Engineering, Legal, TechGov, and CX stakeholders. Influence standards and tooling decisions that affect the ...

Engineering Consultant This is a full-time exempt (40+ hours/week), hybrid and travel-light role ... Knowledge of the utility or energy industry. * Understanding of natural gas and electric building ...

Extended duties include developing knowledge and understanding of engineering practices and procedures and obtaining capabilities in design using CAD software. Essential Duties and Responsibilities:

OR · On-site

Knowledge, Skills and Abilities: Required: * AI-driven SDLC (required): hands-on experience ... Engineering excellence: strong opinions on code quality, testing strategy, CI/CD, and what it means ...

Knowledge of document control processes and company engineering design standards * Familiarity with project delivery methods typical for A/E-led designs * Knowledge of work/information flow between ...

... Knowledge of software engineering principles Preferred : • Ph.D. or Masters preferred • ... Contributing to LLVM or other open source projects • Familiarity with GPU code generation, OpenMP ...

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Knowledge Engineering information

What does a knowledge engineer do?

A knowledge engineer designs, develops, and maintains systems that capture and organize knowledge for artificial intelligence and expert systems. They analyze domain data, create ontologies, and implement knowledge bases using tools like logic programming and semantic technologies. Strong analytical skills and understanding of data modeling are essential for this role.

What is knowledge engineering?

Knowledge engineering is a field within artificial intelligence that focuses on creating systems capable of simulating human decision-making and reasoning. It involves gathering, organizing, and structuring information so that computers can use it to solve complex problems. Knowledge engineers work to build knowledge bases and rule-based systems, often collaborating with domain experts to codify expertise into a form that machines can process. This discipline is fundamental in the development of expert systems, intelligent agents, and modern AI applications.

What is the difference between Knowledge Engineering vs Data Scientist?

AspectKnowledge EngineeringData Scientist
Required CredentialsTypically degrees in computer science, AI, or related fields; certifications in knowledge systemsDegrees in statistics, computer science, or mathematics; certifications in data analysis or machine learning
Work EnvironmentDeveloping knowledge bases, expert systems, and AI applications in tech or research settingsAnalyzing data, building predictive models, and deriving insights in various industries
Employer & Industry UsageUsed in AI development, research institutions, and tech companiesUsed across finance, healthcare, marketing, and tech sectors

While both roles involve working with data and AI, Knowledge Engineers focus on creating structured knowledge bases and expert systems, whereas Data Scientists analyze data to extract insights and build predictive models. Understanding these differences helps in choosing the right career path or job focus.

How does a knowledge engineer typically collaborate with subject matter experts during a project?

Knowledge Engineers frequently work closely with subject matter experts (SMEs) to extract, structure, and formalize domain knowledge into usable formats for AI systems or knowledge bases. This collaboration often involves conducting interviews, facilitating workshops, and reviewing documentation to ensure complex concepts are accurately captured. Effective communication and iterative feedback are key, as Knowledge Engineers must bridge the gap between technical requirements and expert insights. This teamwork helps ensure that the resulting system is both technically sound and aligned with real-world practices.

How much does a knowledge engineer make?

A knowledge engineer's salary typically ranges from $70,000 to $130,000 annually, depending on experience, education, and location. Senior roles or those with specialized skills in AI, machine learning, or data management can earn higher salaries. Many positions also require proficiency with tools like ontologies, semantic web technologies, and knowledge representation languages.

What are the key skills and qualifications needed to thrive as a knowledge engineer?

To thrive as a Knowledge Engineer, you need a strong background in computer science, logic, and data modeling, often supported by a relevant degree. Familiarity with knowledge representation systems, ontologies, semantic web technologies, and tools like Protégé is typically required, along with experience in programming languages such as Python or Java. Strong analytical thinking, problem-solving abilities, and clear communication skills help you collaborate with subject matter experts and translate complex information into structured formats. These skills are critical for building effective knowledge-based systems that drive intelligent decision-making and organizational efficiency.
Infographic showing various Knowledge Engineering job openings in Oregon as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 18% Part Time, 1% Temporary, and 3% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution.

Senior Applied AI Engineer

LTS

OR • Remote

$122K - $161K/yr

Full-time

Posted 9 days ago


Job description

Location: United States - Remote
Clearance: Ability to obtain and maintain a Public Trust

LTS is seeking a highly skilled Senior Applied AI Engineer to focus on continuously improving the intelligence behind the platform. You'll experiment with models, optimize retrieval strategies, refine agent reasoning, evaluate AI performance, and transform emerging AI capabilities into production-ready solutions.

The Agentic AI platform is designed to help engineers understand, analyze, and modernize one of the most consequential legacy software systems still operating today.

Our platform enables engineers to ask questions in plain English and receive explainable, verifiable answers traced directly back to decades of production source code. Rather than replacing engineers, we're building AI that accelerates engineering through transparency, traceability, and intelligent reasoning.

We're building an AI-native engineering platform supporting the modernization of mission-critical healthcare systems serving millions of Veterans nationwide.

The platform is designed for deployment across federal enterprise environments and is being engineered to align with FedRAMP security controls, Zero Trust principles, and federal compliance requirements.

The platform has executive sponsorship, committed users, and a customer investing in long-term modernization. Our engineering team is intentionally small giving every engineer meaningful ownership, and direct influence over product direction.

We don't simply build AI-powered software-we build software with AI. This is not another chatbot.

Using LLMs, autonomous agents, AI-assisted development, parallel workflows, and model-driven engineering is simply how we work.

What You'll Do:

Advance Applied AI Capabilities

  • Design, prototype, and implement production-ready AI capabilities that improve reasoning, accuracy, explainability, and developer productivity.
  • Evaluate emerging LLMs, multimodal models, agent frameworks, and AI techniques to identify opportunities for platform advancement.
  • Rapidly prototype new AI capabilities and transition successful experiments into production.

Optimize Agent Performance

  • Improve autonomous and multi-agent workflows through prompt engineering, reasoning optimization, memory strategies, tool selection, and context management.
  • Continuously refine Retrieval-Augmented Generation (RAG) pipelines, retrieval strategies, embeddings, reranking, and grounding techniques.
  • Improve AI response quality through experimentation, benchmarking, and iterative optimization.

Evaluate AI Systems

  • Develop evaluation frameworks that measure accuracy, groundedness, explainability, latency, and overall AI effectiveness.
  • Build benchmark datasets, automated evaluation pipelines, and performance metrics for production AI systems.
  • Analyze AI failures, hallucinations, retrieval gaps, and reasoning errors to drive continuous improvement.

Knowledge Engineer

  • Collaborate with software engineers to improve knowledge ingestion, document processing, semantic search, embeddings, and enterprise knowledge management.
  • Design approaches that maximize retrieval quality across large technical documentation and source code repositories.
  • Improve how AI agents discover, organize, and reason over enterprise knowledge.

Collaborate Across Engineer

  • Partner closely with AI architects, platform engineers, software engineers, and front-end engineers to improve the overall intelligence of the platform.
  • Share research findings, experimental results, and engineering recommendations with cross-functional teams.
  • Help establish best practices for experimentation, evaluation, and AI quality throughout the organization.

What We're Looking For:

  • Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Engineering, Data Science, or a related technical discipline (or equivalent professional experience).
  • 5+ years of software engineering, applied AI, machine learning, or AI systems development experience.
  • Demonstrated experience developing production AI applications powered by Large Language Models (LLMs).
  • Experience designing and optimizing Retrieval-Augmented Generation (RAG) systems.
  • Experience with prompt engineering, embeddings, semantic search, vector databases, and knowledge retrieval.
  • Experience evaluating AI model performance and implementing experimentation frameworks.
  • Strong programming skills in Python and experience with modern software engineering practices.
  • Experience with AI frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.
  • Experience with using AI coding assistants as part of your daily workflow.
  • Familiarity with REST APIs, cloud-native applications, and distributed software systems.
  • Strong analytical, problem-solving, and communication skills.
  • Intellect and curiosity for AI systems and how they behave.
  • Deep passion for experimenting with new AI techniques.
  • Background in evaluation, explainability, and continuous improvement.
  • Proven success with ownership of difficult technical challenges and collaboration across disciplines.

Nice to Have:

  • Experience optimizing autonomous or multi-agent AI systems.
  • Experience implementing automated AI evaluation frameworks.
  • Experience with OpenAI, Azure OpenAI, Anthropic Claude, Google Vertex AI, AWS Bedrock, or open-source LLMs.
  • Experience with vector databases including Pinecone, Weaviate, Qdrant, Milvus, or Azure AI Search.
  • Experience with Responsible AI, AI governance, safety, and explainability.
  • Familiarity with software engineering tools, code intelligence platforms, or developer productivity solutions.
  • Experience supporting healthcare, Federal Government, or other highly regulated environments.
  • Experience using AI coding assistants and autonomous agents as part of daily software development.

What's In It for You?

  • The Opportunity to support high-visibility federal missions
  • A culture that values innovation, growth, and collaboration
  • Access to cutting-edge tools and technologies
  • Comprehensive benefits for you and your family
  • A career path that rewards ambition and performance

If you're ready to push boundaries, sharpen your skills, and join a team that is passionate about building what's next, we'd love to meet you. Apply today and let's build a future together!