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Knowledge Engineering Jobs in Pittsburgh, PA (NOW HIRING)

System One

GenAI Context Engineer

Pittsburgh, PA · On-site

This role focuses on enabling high quality, grounded, and context aware AI experiences through Retrieval Augmented Generation (RAG), semantic search, metadata engineering, and enterprise knowledge ...

System One

GenAI Context Engineer

Pittsburgh, PA · On-site

This role focuses on enabling high quality, grounded, and context aware AI experiences through Retrieval Augmented Generation (RAG), semantic search, metadata engineering, and enterprise knowledge ...

System One

GenAI Context Engineer

Pittsburgh, PA · On-site

This role focuses on enabling high quality, grounded, and context aware AI experiences through Retrieval Augmented Generation (RAG), semantic search, metadata engineering, and enterprise knowledge ...

System One

GenAI Context Engineer

Pittsburgh, PA · On-site

Required Skills: * 6+ years of experience in Knowledge Engineering, AI Engineering, Enterprise Search, Data Engineering, or Platform Engineering. * Hands-on experience with Retrieval Augmented ...

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Knowledge Engineering information

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How much do knowledge engineering jobs pay per hour?

As of Aug 5, 2026, the average hourly pay for knowledge engineering in Pittsburgh, PA is $30.63, according to ZipRecruiter salary data. Most workers in this role earn between $19.62 and $36.88 per hour, depending on experience, location, and employer.

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.
What are popular job titles related to Knowledge Engineering jobs in Pittsburgh, PA? For Knowledge Engineering jobs in Pittsburgh, PA, the most frequently searched job titles are:
What job categories do people searching Knowledge Engineering jobs in Pittsburgh, PA look for? The top searched job categories for Knowledge Engineering jobs in Pittsburgh, PA are:
What cities near Pittsburgh, PA are hiring for Knowledge Engineering jobs? Cities near Pittsburgh, PA with the most Knowledge Engineering job openings:
Knowledge Engineering jobs near you
Infographic showing various Knowledge Engineering job openings in Pittsburgh, PA as of July 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution, with an average salary of $63,709 per year, or $30.6 per hour.

GenAI Context Engineer

System One

Pittsburgh, PA • On-site

Full-time

Posted 27 days ago

Job description

Job Title: GenAI Context Engineer Duration : Permanent Full Time Location : Strongsville, OH, Dallas, TX, or Pittsburgh, PA. Work Mode : 5 Days Onsite Looking to hire a Context Engineer who will be responsible for designing, building, and optimizing the enterprise knowledge and retrieval foundation that powers Generative AI applications. This role focuses on enabling high quality, grounded, and context aware AI experiences through Retrieval Augmented Generation (RAG), semantic search, metadata engineering, and enterprise knowledge orchestration. The Context Engineer ensures AI systems retrieve the right information, from the right sources, at the right time — securely, accurately, and in alignment with enterprise governance standards. Future duties and responsibilities

  • Design and implement enterprise Retrieval Augmented Generation (RAG) architectures for GenAI platforms and applications.
  • Build and optimize semantic retrieval pipelines, vector search implementations, and contextual grounding frameworks.
  • Develop ingestion pipelines for enterprise knowledge sources including SharePoint, Confluence, Jira, APIs, databases, and document repositories.
  • Define metadata, taxonomy, ontology, chunking, and embedding strategies to improve retrieval relevance and AI response quality.
  • Implement permission aware retrieval and secure knowledge access aligned with enterprise governance and compliance requirements.
  • Design and optimize hybrid search architectures combining vector search, keyword search, and knowledge graph capabilities.
  • Collaborate with Value Engineers, architects, and business stakeholders to translate enterprise knowledge into scalable AI ready knowledge structures.
  • Improve groundedness, citation accuracy, retrieval precision, and hallucination reduction across GenAI solutions.
  • Maintain knowledge lineage, auditability, and contextual traceability for enterprise AI workflows.
  • Support AI evaluation, observability, and continuous improvement initiatives for retrieval quality and search performance.
  • Work closely with governance, security, and compliance teams to ensure responsible and secure enterprise AI knowledge access.
  • Contribute to reusable enterprise knowledge engineering patterns and platform accelerators.
Required qualifications to be successful in this role
  • 6+ years of experience in knowledge engineering, enterprise search, data engineering, AI engineering, or platform engineering roles.
  • Experience building enterprise AI search or knowledge platforms in banking, financial services, healthcare, or other regulated industries.
  • Familiarity with knowledge graphs, ontology modeling, AI observability, and enterprise governance frameworks.
  • Understanding of responsible AI, groundedness evaluation, and enterprise compliance requirements for GenAI systems.
  • Hands on experience with Retrieval Augmented Generation (RAG), semantic search, embeddings, vector databases, and enterprise knowledge systems.
  • Strong programming skills in Python and experience with API based integrations.
  • Experience with GenAI and retrieval technologies such as: o Azure OpenAI / OpenAI o Azure AI Search o LangChain / Semantic Kernel o Elasticsearch / OpenSearch o Vector databases and embedding frameworks
  • Experience designing ingestion pipelines, metadata frameworks, chunking strategies, and contextual retrieval systems.
  • Strong understanding of enterprise data governance, access control, lineage, and permission aware retrieval. Experience integrating enterprise content systems including SharePoint, Confluence, Jira, document repositories, and enterprise APIs.
  • Familiarity with cloud platforms such as Azure, AWS, or GCP and containerized environments.
  • Strong analytical, troubleshooting, and problem solving skills.
  • Excellent communication and collaboration skills with the ability to work across engineering, architecture, governance, and business teams.
Required Skills:
  • LangChain
  • LangGraph
  • LlamaIndex
  • Microsoft Azure AI Solution
  • OpenAI
  • Python
  • Retrieval-Augmented Gen.(RAG)
Ref: #404-IT Pittsburgh