Тип вакансії: Повна зайнятість

Розташування:

We build software for healthcare organizations, from early-stage startups to established digital health vendors, providers and payers. We are looking for a hands-on solution architect who joins presale conversations and turns an ambiguous healthcare idea into a credible architecture, a scoped prototype and an estimate that our delivery team can stand behind. In this role, AI innovation and governance go together: every proposal should be fast to prototype and safe to put in front of a regulator, a compliance officer or a hospital security team.

What you will do:
Join discovery, RFP and technical pitch calls with founders, clinical leaders, operations and IT teams, and explain architecture decisions in plain language.
Design architectures for EHR/EMR integration, patient data exchange and healthcare workflows such as scheduling, eligibility verification, referrals, prior authorization, claims and revenue cycle management (RCM).
Build AI-assisted prototypes and demos on sandboxes and synthetic data, and state clearly what is real, what is simulated and what remains for production.
Define the AI governance approach for each proposal: data handling, model selection, human oversight, validation, monitoring and audit trail.
Separate a proof of concept from an MVP, choose the single use case that proves feasibility, and defend that choice.
Produce estimates (phases, stories, assumptions, risks) together with Delivery, and rebuild them when scope changes.
Review client-supplied architecture documents and AI-generated specs critically, keeping what is sound and challenging what is not.
Complete customer security questionnaires and due diligence, and prepare HIPAA, SOC 2 and HITRUST evidence for prospects.
Support partner conversations with integration platforms, clearinghouses and EHR vendors.
Hand over every won project to Delivery with a clean architecture, documented assumptions and open risks.
Healthcare domain expertise:
Systems and data: hands-on experience with EHR/EMR platforms (Epic, Cerner/Oracle Health, athenahealth, eClinicalWorks, NextGen or similar), practice management and patient portals.
Standards: FHIR R4, SMART on FHIR, CDS Hooks, Bulk FHIR, HL7 v2 (ADT, ORM, ORU), C-CDA/CCD, and DICOM basics.
Administrative and financial flows: X12 270/271 (eligibility), 278 (referrals and prior authorization), 837/835 (claims and remittance), clearinghouses, payer and provider connectivity, denial management and the RCM lifecycle.
Coding and terminology: ICD-10, CPT/HCPCS, SNOMED CT, LOINC, RxNorm, NPI, and the mapping and normalization problems between them.
Data governance: PHI and PII handling, minimum necessary access, de-identification (Safe Harbor and Expert Determination), consent management (including 42 CFR Part 2 where relevant), master patient index and patient matching, and audit logging.
Regulation and policy: HIPAA Privacy, Security and Breach Notification rules, 21st Century Cures Act and information blocking, ONC/ASTP certification (HTI rules), CMS interoperability and prior authorization rules, and TEFCA.
Quality and value-based care: HEDIS, eCQM, CQL, care gaps, risk adjustment and population health analytics.
AI governance in regulated environments:
Practical knowledge of frameworks and rules such as NIST AI RMF, ISO/IEC 42001, ONC decision support transparency requirements, FDA guidance on clinical decision support and software as a medical device, and the EU AI Act for clients operating in Europe.
Experience designing human-in-the-loop controls, escalation to clinical or operational staff, and clear boundaries on what an AI agent may and may not do.
Ability to handle PHI in LLM workflows: vendor BAAs, data residency, prompt and log redaction, no training on customer data, retention limits and access control.
Methods for validating and monitoring AI: evaluation sets, hallucination and bias testing, drift monitoring, versioning of models and prompts, and incident response.
Documentation habits that regulators and buyers expect: model cards, risk assessments, data lineage, decision logs and explainability for non-technical reviewers.
Technical requirements:
8+ years in software engineering and solution architecture, including delivery of SaaS and mobile products at scale.
3+ years in healthtech integration or digital health product work.
Cloud-native architecture on AWS, Azure, or GCP using HIPAA-eligible services, with encryption, key management, network isolation, and least-privilege access.
Integration engines and platforms (Mirth/NextGen Connect, Rhapsody, Redox, Health Gorilla, Medplum, Stedi or similar), including API design, event-driven patterns and reliable message handling.
Data engineering: ETL/ELT pipelines, SQL and NoSQL, data warehouses, streaming, analytics and BI.
Security practices: OAuth 2.0 and OIDC, RBAC, secrets management, secure SDLC, threat modeling and penetration test readiness.
Experience with SOC 2 or HITRUST programs, and with third-party risk questionnaires from health systems and payers.
Hands-on AI engineering: LLM APIs, agent orchestration, RAG, speech and messaging channels, cost and latency management, and prompt evaluation.
Rapid prototyping with AI coding tools, and the judgment to know when generated code must be rebuilt from a clean specification.
Reliability and observability for clinical and operational workflows: monitoring, auditability, disaster recovery and SLAs.
Mobile and web accessibility awareness (WCAG, Section 508) for patient-facing products.
Testing strategy for regulated software: validation evidence, traceability and change control.
Nice to have:
Experience at an EHR vendor, clearinghouse, payer, health system or healthtech startup.
Voice AI, SMS or omnichannel patient engagement experience.
Experience with ambient documentation, coding assistance or prior authorization automation.
Knowledge of medical device software standards (IEC 62304, ISO 14971).
Public speaking, writing or open-source contributions in healthcare IT.

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