Application Implementation Engineer – AI Implementation

Lumexa Imaging

. Lead technical evaluation, validation, implementation, integration, and clinical adoption of third-party clinical AI solutions .

Posted 9/22/2026full-timeRemote • North Carolina • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in clinical imaging informatics and AI deployment, with a strong focus on DICOM, HL7, and PACS/RIS architecture. Proficient in Python for validation pipelines and experienced in designing AI performance validation studies.

Highest-signal resume keywords

Clinical Imaging InformaticsDICOM Routing and AnonymizationPython Scripting for ValidationAI Performance Validation StudiesClinical AI Software Installation

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Applicant Tracking System Keywords

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Hard Skills

Clinical Imaging WorkflowsAI Validation MethodologyData Extraction and ComparisonAutomated De-identification PipelinesLLM/NLP TechniquesClinical AI Integration DesignRisk Assessment and ManagementTechnical Evaluation and ValidationCloud Environment ConfigurationPerformance Metrics Analysis

Soft Skills

Collaboration with Clinical LeadershipProject Scoping and PrioritizationIndependent Operation in Ambiguous Environments

Tools & Technologies

Laurel Bridge CompassRSNA CTPAWS Comprehend MedicalOCR-based Pixel MaskingAWSAzureGCP

Certifications & Qualifications

CIIP Certification

Industry Keywords

FDA 510(k) ClearanceCPT Reimbursement CodesHIPAA ComplianceClinical AI GovernanceImaging AI Vendor Solutions

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformPythonRay

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

5+ years of experience in clinical imaging informatics, radiology AI deployment, or imaging AI vendor field engineering
Hands-on experience installing and configuring clinical AI software across sandbox, edge server, on-prem, or cloud environments, including data routing, anonymization, and system configuration
Working knowledge of clinical imaging workflows, including how radiologists read studies, interpret findings, and finalize reports
Fluency with DICOM, HL7, FHIR, and PACS/RIS architecture
Hands-on Python or equivalent scripting skills for validation pipelines, data extraction, and comparison analysis
Experience with LLM/NLP techniques for text comparison, semantic similarity, or structured information extraction from clinical reports
Experience designing and executing AI performance validation studies, including metrics, ground truth, and study methodology
Experience building or improving automated de-identification and data preparation pipelines, including PACS cohort selection, DICOM header and burned-in pixel anonymization, paired report de-identification, and secure vendor packaging
Hands-on experience with DICOM routing and anonymization platforms such as Laurel Bridge Compass or RSNA CTP, alongside complementary tooling such as Presidio, AWS Comprehend Medical, or OCR-based pixel masking, strongly desired
Ability to collaborate with clinical leadership and technical IT stakeholders
Ability to operate independently in ambiguous, fast-moving environments with minimal oversight
Strong project scoping, prioritization, and execution skills across multiple concurrent evaluations and projects
Preferred: clinical knowledge of CT, MRI, mammography, X-ray, and ultrasound workflows
Preferred: clinical imaging AI vendor solutions, field, or implementation engineering experience
Preferred: CIIP certification or equivalent
Preferred: experience as a radiology technologist, imaging informaticist, or radiology research engineer
Preferred: experience with AWS, Azure, or GCP
Preferred: familiarity with FDA 510(k) clearance and CPT reimbursement codes
Preferred: HIPAA and healthcare compliance knowledge
Preferred: advanced degree in biomedical engineering, medical imaging, computer science, or related field