Solutions Architect

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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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