AI Architect - Cigna Healthcare
Cigna · 2 Locations · US
Software Engineering
–
Not yet analysed
Demand82
Career growth78
Healthcare fit24
Remote potential50
5-year outlook
Senior AI architecture roles in large payers typically sit in the upper technical compensation bands and should remain strong as insurers invest in AI. Exact figures are not stated in this posting and should be verified directly.
Clinical license
Not required
Applicant reach
narrow
Seniority
senior
Employment type
–
Date found
2026-10-22
Intelligence details
Flexible work details
- Eligible countries
- United States
Eligibility, licensing, and location restrictions can change. Always confirm details on the source posting before applying.
Why this is worth applying to
Only worth pursuing if you are a clinician who has genuinely built and shipped AI/ML systems at scale — the title implies senior engineering depth. For most clinicians this is a stretch role rather than a realistic transition target.
Who should apply
Physician-engineers and clinician-informaticists with hands-on ML/software engineering experience, former clinical data scientists moving into architecture, and healthcare graduates with a computer science or ML engineering background plus payer domain exposure.
CV angle
Lead with technical delivery: AI/ML systems designed and deployed, cloud platforms, data pipelines, and architecture decisions, quantified by scale and impact. Then layer healthcare domain credibility — claims, EHR/interoperability standards (HL7, FHIR), privacy and regulatory constraints (HIPAA), and clinical workflow knowledge — framing clinical experience as domain context rather than the core qualification.
Skills to highlight
- Enterprise and solution architecture
- Machine learning / LLM system design
- Cloud platforms (AWS/Azure/GCP)
- MLOps and model lifecycle management
- Data governance and responsible AI
- Healthcare data standards (HL7, FHIR) and HIPAA
- Stakeholder and cross-functional leadership
Role summary
A senior technical role designing AI and machine learning architecture for a large health insurer, spanning model/platform design, integration with enterprise data systems, and governance of AI use cases across the business.
We publish our own intelligence summary, never the full job description. Always confirm details with the employer when you apply.