AI Engineer

• Design, develop, and maintain interactive dashboards and analytical applications using AI Foundry components including Ontology, Workshop, Slate, and Contour
• Build and optimize data pipelines, transformations, and data models for enterprise analytics and operational use cases
• Create and manage Ontology objects, relationships, actions, and operational workflows
• Build user-friendly applications and dashboards using Workshop and Slate for business stakeholders
• Configure and maintain Contour-based reporting and analytical views
• Manage user access, permissions, and security roles within Foundry environments
• Perform Foundry branching activities and support code migration using Marketplace and deployment workflows
• Configure and maintain data connections, syncs, and external system integrations
• Monitor pipeline performance, troubleshoot failures, and optimize data processing jobs
• Collaborate with business users, data engineers, architects, and product owners to gather requirements and deliver scalable solutions
• Support CI/CD processes, testing, release management, and platform governance best practices
• Provide production support and ongoing enhancements for Palantir applications and data products

• Lead the technical design of new agent capabilities, from ambiguous product intent to shipped system
• Build and operate services end to end, from event trigger through LLM reasoning to persisted, surfaced results
• Extend and shape the shared agent framework, including orchestration, tool use, structured generation, and observability
• Design and tune RAG and embedding pipelines on vector search in the operational database
• Design evaluations for new agent behaviors and gate prompt changes on them
• Own reliability, including failure classification, idempotency, DLQ handling, and rollout safety for AI features in production
• Set standards in design and code review and mentor engineers ramping onto the AI stack
• Evaluate emerging models and techniques and integrate effective ones into the platform
• Work with TypeScript, Go, Vercel AI SDK, Effect, gRPC, tRPC, Kafka, SQS, Lambda, EKS on AWS, Datadog, feature-flagged rollouts, and infrastructure as code

Senior AI Engineer II – Global Commercial Services Technology

• Build production agentic AI services end to end, from event trigger through LLM reasoning to persisted, surfaced results
• Contribute to the shared agent framework, including orchestration, tool use, structured generation, and observability
• Build RAG and embedding pipelines using vector search in the operational database
• Implement evaluations for new agent behaviors and use them to gate prompt changes
• Build for reliability through failure classification, idempotency, DLQ handling, and safe AI-feature rollouts
• Participate in technical design discussions and code reviews, growing toward owning designs
• Help onboard engineers onto the AI stack
• Work across TypeScript and Go services, gRPC and tRPC APIs, Kafka, SQS, Lambda, EKS on AWS, feature-flagged rollouts, and infrastructure as code

Senior AI Engineer – Global Commercial Services

• Lead technical evaluation, validation, implementation, integration, and clinical adoption of third-party clinical AI solutions
• Install, configure, and maintain clinical AI software across sandbox, edge server, on-premise, and cloud environments
• Establish DICOM routing, HL7 messaging, and de-identification workflows for images and reports
• Design and execute structured AI validation studies using appropriate metrics such as sensitivity, specificity, discrepancy rates, and clinical/workflow impact
• Build LLM/NLP-based frameworks to compare AI outputs with radiologist ground truth
• Develop Lumexa's reusable clinical AI validation playbook and automated evaluation tooling
• Author evidence-based go/no-go recommendations for the AI Governance Council, including risk assessment and deployment scope
• Partner with clinical leadership to define ground truth, acceptance thresholds, workflow fit, and clinical priorities
• Translate validated AI capabilities into deployment-ready integration designs for radiologist workflows
• Map current- and future-state workflows and identify workflow risks, change-management considerations, and adoption barriers
• Support production handoff with Clinical Applications across RIS, PACS, reporting, and related clinical technology systems
• Assess infrastructure, edge server, network, compute, storage, security, encryption, access controls, and HIPAA compliance requirements
• Evaluate vendor technical maturity, scalability, supportability, regulatory status, performance claims, and integration complexity
• Provide technical input for vendor contract negotiations, including SLAs, model retraining cadence, and performance guarantees
• Stay current on clinical imaging AI vendors, modalities, FDA clearances, and CPT reimbursement codes
• Identify opportunities to expand Lumexa's clinical AI portfolio and contribute to AI governance and validation thought leadership

Application Implementation Engineer – AI Implementation

• Define and execute technology strategy, roadmaps, and investment priorities aligned to enterprise and business objectives
• Partner with business, digital, marketing, operations, and technology leaders to identify innovation and transformation opportunities
• Advise senior stakeholders on customer engagement technologies, platform strategy, and AI-enabled solutions
• Promote a product-centric culture focused on business outcomes, customer value, and continuous improvement
• Lead multiple product and engineering teams responsible for enterprise communication and AI platform capabilities
• Drive delivery of scalable, resilient, and secure solutions leveraging Salesforce Marketing Cloud, AWS, enterprise data platforms, and modern AI technologies
• Oversee platform modernization initiatives improving customer engagement, personalization, automation, and operational efficiency
• Establish priorities, manage funding and resources, and ensure successful execution of strategic initiatives
• Monitor business outcomes, delivery metrics, and platform performance to inform investment and roadmap decisions
• Lead development and adoption of enterprise Generative AI and Agentic AI capabilities
• Identify and prioritize high-value AI use cases with product and business leaders
• Drive responsible AI practices, governance standards, and scalable platform adoption
• Build, lead, and develop teams of product managers, engineers, architects, and technical leaders
• Foster collaboration, innovation, accountability, and continuous learning
• Develop organizational capabilities across cloud engineering, digital platforms, marketing technology, and AI solutions
• Coach leaders and team members while promoting engineering excellence and operational discipline

Senior Manager, IT Delivery – Enterprise Communications, Agentic AI Platforms

• Design, develop, deploy, and integrate machine learning models, AI tools, and analytics products into Alliance systems and workflows
• Improve member health, quality of services, and administrative processes through AI/ML solutions
• Establish foundational platforms and processes for developing and sustaining machine learning and artificial intelligence capabilities
• Collaborate with technical managers and cross-functional teams to build and deploy AI/ML models and solutions
• Analyze medical claims, electronic health records, and care management records to identify opportunities for process and care improvement
• Develop analytics problem statements and modeling objectives from care management and administrative use cases
• Iteratively develop data pipelines, AI/ML models, and supporting code
• Evaluate internal and third-party AI/ML models and tools using evaluation pipelines and appropriate metrics
• Collaborate with application development, external vendors, and internal IT teams to deploy and sustain AI/ML models and tools
• Design and implement model/tool monitoring reports or dashboards
• Help establish internal processes governing AI/ML development, evaluation, deployment, and sustainment
• Contribute to internal and/or cloud-based infrastructure for AI/ML projects
• Develop reusable data processing or modeling libraries and reusable features
• Adhere to patient privacy, confidentiality, information security, legal, regulatory, internal policy, and ethical requirements
• Communicate with technical and non-technical stakeholders and present analyses, evaluations, and recommendations
• Share knowledge about AI/ML tools, case studies, impacts, and opportunities
• Stay current on relevant AI/ML software, tools, modeling techniques, advances, and managed-care use cases

AI Engineer jobs in North Carolina

AI Engineer

Senior AI Engineer II – Global Commercial Services Technology

Senior AI Engineer – Global Commercial Services

Application Implementation Engineer – AI Implementation

Senior Manager, IT Delivery – Enterprise Communications, Agentic AI Platforms

Artificial Intelligence Engineer