Principal AI Platform Architect: Knowledge & Context

Claritev Corporation; United States; Sur place; USD 190 000 - 210 000; Plein temps

Résumé du poste

Claritev Corporation in the United States is seeking a Principal Applied AI Engineer to lead the architecture, development, deployment, and operation of production AI applications, services, and platforms.

You will design knowledge graphs, ontologies, and retrieval systems, connect them to AI agents, and collaborate with business and engineering teams to deliver scalable enterprise AI across systems.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related quantitative field.
• Master’s degree or PhD preferred.
• 10+ years of hands-on experience in software engineering, machine learning engineering, applied AI, or a related technical discipline.
• 5+ years of experience designing and delivering production‑grade ML or AI systems.
• 3+ years of experience building with generative AI, LLMs, RAG, and/or agentic AI systems.
• Demonstrated experience leading complex technical initiatives from concept through production deployment and measurable business impact.
• Strong software engineering skills, including expert‑level Python proficiency and experience designing scalable services, APIs, and distributed systems.
• Foundation in machine learning, statistics, and optimization.
• Experience with agentic AI frameworks and patterns, such as LangGraph, LangChain, etc.
• Experience designing AI agents with tool use, planning, orchestration, memory, and guardrails.
• Experience developing evaluation and observability capabilities for LLM and ML systems, including accuracy, reliability, safety, latency, and cost.
• Understanding of MLOps/LLMOps and lifecycle management practices including CI/CD, model and prompt versioning, monitoring, experimentation, and incident troubleshooting.
• Hands‑on experience with knowledge graphs and graph data modeling, including LPG and RDF and databases like Neo4j, AWS Neptune, or similar.
• Strong understanding of ontologies, semantic modeling, entity and relationship modeling.
• Experience with vector databases, embeddings, RAG, and approaches like GraphRAG.
• Experience with graph query languages and tooling such as Cypher, SPARQL, RDFS, OWL, SHACL, Protégé, and comparable technologies.
• Experience designing or implementing systems for knowledge extraction, entity and relationship extraction, provenance, metadata enrichment, or automated knowledge curation from heterogeneous enterprise sources.
• Understanding of graph algorithms such as community detection and pathfinding.
• Strong problem‑solving, critical‑thinking, communication, and organizational skills.
• Ability to communicate complex technical concepts clearly to technical and non‑technical stakeholders.
• Ability to operate effectively in a fast‑moving, cross‑functional environment.
• Experience in healthcare, health technology, insurance, claims, or other regulated industries.
• Experience building AI systems that process PHI/PII.
• Experience with analytical data architectures and enterprise metadata/catalog systems.
• Experience with deep‑learning frameworks such as PyTorch or TensorFlow.

Responsabilités

• Lead the architecture, development, deployment, and operation of production AI applications, services, and platforms.
• Design 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.
• Establish reusable frameworks, APIs, MCPs, code components, and engineering patterns that enable teams to build and deploy AI solutions efficiently and consistently.
• Drive end‑to‑end delivery from prototype through production, including integration with enterprise systems, monitoring, observability, evaluation, and ongoing improvement.
• Build 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.
• Work 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.
• Partner with AI engineers, software engineers, data engineers, infrastructure teams, security, governance, and subject matter experts to integrate knowledge and agentic solutions into production.
• Establish quality standards for both AI agents and knowledge, including offline and online evaluations, reliability, latency, cost, safety, and performance.
• Ensure secure and responsible use of AI, including privacy, PHI/PII protection, explainability, auditability, and compliance with HIPAA and applicable data‑governance requirements.
• Provide technical leadership across complex, cross‑functional initiatives; influence architecture and engineering decisions beyond an individual project.
• Mentor engineers and data scientists to promote a culture of technical excellence, continuous learning, and pragmatic innovation.

Connaissances

Python

AI proficiency

LangGraph

LangChain

production engineering

architectural design

leadership

systems thinking

communication

problem solving

Formation

Bachelor's degree in Computer Science, Engineering, Data Science, or related field

Master's degree or PhD preferred

Outils

Neo4j / AWS Neptune

Cypher / SPARQL

Protégé / SHACL

PyTorch / TensorFlow

LNG frameworks: LangGraph / LangChain

Graph databases

Description du poste

Claritev Corporation in the United States is seeking a Principal Applied AI Engineer to lead the architecture, development, deployment, and operation of production AI applications, services, and platforms.

You will design knowledge graphs, ontologies, and retrieval systems, connect them to AI agents, and collaborate with business and engineering teams to deliver scalable enterprise AI across systems.