{"schemaVersion":"jobsearcher.job.v1","id":"5a71e32bb5e778b170a6fda3","url":"https://jobsearcher.com/jobs/5a71e32bb5e778b170a6fda3","canonicalUrl":"https://jobsearcher.com/jobs/5a71e32bb5e778b170a6fda3","title":"Principal Applied AI & Knowledge Engineer","description":"At Claritev, our mission is to simplify healthcare workflows, improve transparency, and bend the healthcare cost curve. We believe that data, technology, and AI can fundamentally transform how healthcare operates by automating complex workflows, improving decision-making, and reducing unnecessary costs across the system.\n By combining deep healthcare expertise with advanced analytics and AI, we help payers, providers, and employers operate more efficiently and deliver better outcomes for the people they serve.\n We are bold in our thinking, rigorous in execution, and committed to service excellence for every stakeholder. Our culture values innovation, accountability, diversity of thought, and collaboration.\n Join us as we accelerate our transformation into a leading technology and AI-driven company shaping the future of healthcare. \nJOB SUMMARY\n\nWe are seeking Principal Applied AI Engineer to help build and evolve Claritev's AI platform and implement high-impact AI opportunities. One initial focus of this role is to help build and evolve the enterprise Context & Knowledge Layer within the AI platform, which enables AI agents and applications to efficiently and accurately contextualize our data, products, business processes, enterprise systems, industry concepts, and institutional knowledge. Additionally, the role will have opportunities to contribute to other parts of Claritev's AI platform, products, and workflows.\nThis is a hands‑on Principal‑level engineering role that also involves working directly with technical and business stakeholders to understand requirements, make architecture decisions, and turn ambiguous needs into scalable production systems. You will design and build production capabilities while helping establish the patterns, tooling, and engineering practices used to create, operate, maintain, govern, evaluate, and continuously adapt enterprise agentic AI, knowledge, and context.\nThe ideal candidate combines broad proficiency in modern AI with a background in context and knowledge systems. You should be highly proficient in generative and agentic AI while also bringing practical experience with knowledge graphs, ontologies, semantic technologies, and retrieval. A strong understanding of software and data architecture and production engineering will also be necessary.\n\nJOB ROLES AND RESPONSIBILITIES\n\nLead the architecture, development, deployment, and operation of production AI applications, services, and platforms.\nDesign and implement knowledge graphs, ontologies, semantic models, RAG retrieval systems, context graphs, human‑in‑the‑loop controls, and mechanisms that connect them to AI agents.\nEstablish reusable frameworks, APIs, MCPS, code components, and engineering patterns that enable teams to build and deploy AI solutions efficiently and consistently.\nDrive end‑to‑end delivery from prototype through production, including integration with enterprise systems, monitoring, observability, evaluation, and ongoing improvement.\nBuild agentic workflows that manage ingestion, extraction, normalization, linking, validation, curation, governance, and continuous update of enterprise knowledge from heterogeneous sources including structured and unstructured data.\nWork directly with business and operations stakeholders to discover domain concepts, intents, workflows, constraints, and tacit knowledge and translate them into technical representations and platform capabilities.\nPartner with AI engineers, software engineers, data engineers, infrastructure teams, security, governance, and subject matter experts to integrate knowledge and agentic solutions into production.\nEstablish quality standards for both AI agents and knowledge, including offline and online evaluations, reliability, latency, cost, safety, and performance.\nEnsure secure and responsible use of AI, including privacy, PHI/PII protection, explainability, auditability, and compliance with HIPAA and applicable data‑governance requirements.\nProvide technical leadership across complex, cross‑functional initiatives; influence architecture and engineering decisions beyond an individual project.\nMentor engineers and data scientists to promote a culture of technical excellence, continuous learning, and pragmatic innovation.\n\nREQUIREMENTS (Education, Experience, and Training)\nEducation\n\nBachelor's degree in Computer Science, Engineering, Data Science, or a related quantitative field required.\nMaster's degree or PhD preferred.\n\nExperience\n\n10+ years of hands‑on experience in software engineering, machine learning engineering, applied AI, or a related technical discipline.\n5+ years of experience designing and delivering production‑grade ML or AI systems.\n3+ years of experience building with generative AI, LLMs, RAG, and/or agentic AI systems.\nDemonstrated experience leading complex technical initiatives from concept through production deployment and measurable business impact.\n\nGeneral Technical Skills\n\nStrong software engineering skills, including expert‑level Python proficiency and experience designing scalable services, APIs, and distributed systems.\nFoundation in machine learning, statistics, and optimization.\nExperience with agentic AI frameworks and patterns, such as LangGraph, LangChain, etc.\nExperience designing AI agents with tool use, planning, orchestration, memory, and guardrails.\nExperience developing evaluation and observability capabilities for LLM and ML systems, including accuracy, reliability, safety, latency, and cost.\nUnderstanding of MLOps/LLMOps and lifecycle management practices including CI/CD, model and prompt versioning, monitoring, experimentation, and incident troubleshooting.\n\nContext and Knowledge Skills\n\nHands‑on experience with knowledge graphs and graph data modeling, including a good understanding of both labeled property graph (LPG) and RDF‑based approaches, and familiarity with databases such as Neo4j, AWS Neptune, or similar.\nStrong understanding of ontologies, semantic modeling, entity and relationship modeling, schema evolution, and knowledge representation concepts.\nExperience with vector databases, embeddings, RAG, and approaches like GraphRAG.\nExperience with graph query languages and tooling such as Cypher, SPARQL, RDFS, OWL, SHACL, Protege, and comparable technologies.\nExperience designing or implementing systems for knowledge extraction, entity resolution, relationship extraction, provenance, metadata enrichment, or automated knowledge curation from heterogeneous enterprise sources.\nUnderstanding of graph algorithms such as community detection and pathfinding.\n\nOther Skills\n\nStrong problem‑solving, critical‑thinking, communication, and organizational skills.\nAbility to communicate complex technical concepts clearly to technical and non‑technical stakeholders.\nAbility to operate effectively in a fast‑moving, cross‑functional environment.\n\nPreferred Qualifications\n\nExperience in healthcare, health technology, insurance, claims, or other regulated industries.\nExperience building AI systems that process sensitive data, including PHI or PII.\nExperience with analytical data architectures and enterprise metadata/catalog systems.\nExperience with deep‑learning frameworks such as PyTorch or TensorFlow.\n\nCOMPENSATION\nThe salary range for this position is $190k - 210k. Specific offers take into account a candidate's education, experience and skills, as well as the candidate's work location and internal equity. This position is also eligible for health insurance, 401k and bonus opportunity.\nWhy Claritev?\nHealthcare is complex. We help make it clearer.\nAt Claritev, you'll do work that matters. Together, we're helping make healthcare more transparent and affordable for all through the power of data, technology, and expertise. We offer meaningful opportunities to grow your career, collaborate with talented colleagues, and make an impact on the clients and communities we serve. If you're looking for purpose, growth, and a team that succeeds together, you'll find it here.\nWhat Guides Us\nAt Claritev, innovation, agility, and a focus on results drive our success. We embrace bold thinking, work as one team, take ownership, and strive for excellence in everything we do - creating meaningful impact for our clients, communities, and each other. \n#J-18808-Ljbffr","company":"Multiplan","rawCompany":"multiplan","city":"McLean","state":"VA","isRemote":false,"isActive":true,"createdAt":"2026-10-03T03:44:57.747Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-1211.00","title":"Computer Systems Analysts","slug":"computer-systems-analysts"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Principal Applied AI & Knowledge Engineer","description":"At Claritev, our mission is to simplify healthcare workflows, improve transparency, and bend the healthcare cost curve. We believe that data, technology, and AI can fundamentally transform how healthcare operates by automating complex workflows, improving decision-making, and reducing unnecessary costs across the system.\n By combining deep healthcare expertise with advanced analytics and AI, we help payers, providers, and employers operate more efficiently and deliver better outcomes for the people they serve.\n We are bold in our thinking, rigorous in execution, and committed to service excellence for every stakeholder. Our culture values innovation, accountability, diversity of thought, and collaboration.\n Join us as we accelerate our transformation into a leading technology and AI-driven company shaping the future of healthcare. \nJOB SUMMARY\n\nWe are seeking Principal Applied AI Engineer to help build and evolve Claritev's AI platform and implement high-impact AI opportunities. One initial focus of this role is to help build and evolve the enterprise Context & Knowledge Layer within the AI platform, which enables AI agents and applications to efficiently and accurately contextualize our data, products, business processes, enterprise systems, industry concepts, and institutional knowledge. Additionally, the role will have opportunities to contribute to other parts of Claritev's AI platform, products, and workflows.\nThis is a hands‑on Principal‑level engineering role that also involves working directly with technical and business stakeholders to understand requirements, make architecture decisions, and turn ambiguous needs into scalable production systems. You will design and build production capabilities while helping establish the patterns, tooling, and engineering practices used to create, operate, maintain, govern, evaluate, and continuously adapt enterprise agentic AI, knowledge, and context.\nThe ideal candidate combines broad proficiency in modern AI with a background in context and knowledge systems. You should be highly proficient in generative and agentic AI while also bringing practical experience with knowledge graphs, ontologies, semantic technologies, and retrieval. A strong understanding of software and data architecture and production engineering will also be necessary.\n\nJOB ROLES AND RESPONSIBILITIES\n\nLead the architecture, development, deployment, and operation of production AI applications, services, and platforms.\nDesign and implement knowledge graphs, ontologies, semantic models, RAG retrieval systems, context graphs, human‑in‑the‑loop controls, and mechanisms that connect them to AI agents.\nEstablish reusable frameworks, APIs, MCPS, code components, and engineering patterns that enable teams to build and deploy AI solutions efficiently and consistently.\nDrive end‑to‑end delivery from prototype through production, including integration with enterprise systems, monitoring, observability, evaluation, and ongoing improvement.\nBuild agentic workflows that manage ingestion, extraction, normalization, linking, validation, curation, governance, and continuous update of enterprise knowledge from heterogeneous sources including structured and unstructured data.\nWork directly with business and operations stakeholders to discover domain concepts, intents, workflows, constraints, and tacit knowledge and translate them into technical representations and platform capabilities.\nPartner with AI engineers, software engineers, data engineers, infrastructure teams, security, governance, and subject matter experts to integrate knowledge and agentic solutions into production.\nEstablish quality standards for both AI agents and knowledge, including offline and online evaluations, reliability, latency, cost, safety, and performance.\nEnsure secure and responsible use of AI, including privacy, PHI/PII protection, explainability, auditability, and compliance with HIPAA and applicable data‑governance requirements.\nProvide technical leadership across complex, cross‑functional initiatives; influence architecture and engineering decisions beyond an individual project.\nMentor engineers and data scientists to promote a culture of technical excellence, continuous learning, and pragmatic innovation.\n\nREQUIREMENTS (Education, Experience, and Training)\nEducation\n\nBachelor's degree in Computer Science, Engineering, Data Science, or a related quantitative field required.\nMaster's degree or PhD preferred.\n\nExperience\n\n10+ years of hands‑on experience in software engineering, machine learning engineering, applied AI, or a related technical discipline.\n5+ years of experience designing and delivering production‑grade ML or AI systems.\n3+ years of experience building with generative AI, LLMs, RAG, and/or agentic AI systems.\nDemonstrated experience leading complex technical initiatives from concept through production deployment and measurable business impact.\n\nGeneral Technical Skills\n\nStrong software engineering skills, including expert‑level Python proficiency and experience designing scalable services, APIs, and distributed systems.\nFoundation in machine learning, statistics, and optimization.\nExperience with agentic AI frameworks and patterns, such as LangGraph, LangChain, etc.\nExperience designing AI agents with tool use, planning, orchestration, memory, and guardrails.\nExperience developing evaluation and observability capabilities for LLM and ML systems, including accuracy, reliability, safety, latency, and cost.\nUnderstanding of MLOps/LLMOps and lifecycle management practices including CI/CD, model and prompt versioning, monitoring, experimentation, and incident troubleshooting.\n\nContext and Knowledge Skills\n\nHands‑on experience with knowledge graphs and graph data modeling, including a good understanding of both labeled property graph (LPG) and RDF‑based approaches, and familiarity with databases such as Neo4j, AWS Neptune, or similar.\nStrong understanding of ontologies, semantic modeling, entity and relationship modeling, schema evolution, and knowledge representation concepts.\nExperience with vector databases, embeddings, RAG, and approaches like GraphRAG.\nExperience with graph query languages and tooling such as Cypher, SPARQL, RDFS, OWL, SHACL, Protege, and comparable technologies.\nExperience designing or implementing systems for knowledge extraction, entity resolution, relationship extraction, provenance, metadata enrichment, or automated knowledge curation from heterogeneous enterprise sources.\nUnderstanding of graph algorithms such as community detection and pathfinding.\n\nOther Skills\n\nStrong problem‑solving, critical‑thinking, communication, and organizational skills.\nAbility to communicate complex technical concepts clearly to technical and non‑technical stakeholders.\nAbility to operate effectively in a fast‑moving, cross‑functional environment.\n\nPreferred Qualifications\n\nExperience in healthcare, health technology, insurance, claims, or other regulated industries.\nExperience building AI systems that process sensitive data, including PHI or PII.\nExperience with analytical data architectures and enterprise metadata/catalog systems.\nExperience with deep‑learning frameworks such as PyTorch or TensorFlow.\n\nCOMPENSATION\nThe salary range for this position is $190k - 210k. Specific offers take into account a candidate's education, experience and skills, as well as the candidate's work location and internal equity. This position is also eligible for health insurance, 401k and bonus opportunity.\nWhy Claritev?\nHealthcare is complex. We help make it clearer.\nAt Claritev, you'll do work that matters. Together, we're helping make healthcare more transparent and affordable for all through the power of data, technology, and expertise. We offer meaningful opportunities to grow your career, collaborate with talented colleagues, and make an impact on the clients and communities we serve. If you're looking for purpose, growth, and a team that succeeds together, you'll find it here.\nWhat Guides Us\nAt Claritev, innovation, agility, and a focus on results drive our success. We embrace bold thinking, work as one team, take ownership, and strive for excellence in everything we do - creating meaningful impact for our clients, communities, and each other. \n#J-18808-Ljbffr","datePosted":"2026-10-03T03:44:57.747Z","dateModified":"2026-10-03T03:44:57.747Z","hiringOrganization":{"@type":"Organization","name":"Multiplan","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"McLean","addressRegion":"VA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"5a71e32bb5e778b170a6fda3"},"url":"https://jobsearcher.com/jobs/5a71e32bb5e778b170a6fda3"}}