Principle Solution Architect/ Director

We are seeking an experienced Engineering Lead / Principal Solution Architect to drive the architectural vision, technical strategy, and engineering execution of our next-generation healthcare technology platform. This role combines deep expertise in Healthcare ITincluding Electronic Health Records (EHR), Practice Management Systems (PMS), and Revenue Cycle Management (RCM) with modern cloud-native architecture, Agentic AI automation, and hands-on engineering leadership.

In this dual-track leadership role, you will own the end-to-end architectural roadmap, translating business objectives into scalable target-state architectures, high-level and low-level design specifications, and highly available deployment models. You will also lead, mentor, and scale distributed engineering teams across software development, solution architecture, AI/RPA, and cloud infrastructure.

Working closely with product management, clinical stakeholders, executive leadership, and customer delivery teams, you will establish a culture of technical excellence, architectural governance, continuous delivery, operational resilience, and cross-functional collaboration.

Key Responsibilities:-

1. Engineering Leadership, Governance & Delivery

• Engineering Team Leadership: Lead, mentor, and scale distributed, cross-functional engineering teams spanning frontend, backend, AI/automation, architecture, and cloud infrastructure.
• Engineering Execution & Delivery: Establish Agile operating models, sprint planning, capacity planning, technical roadmaps, and delivery governance across multiple product lines.
• Engineering Excellence: Define and enforce engineering standards for code quality, design reviews, testing, security, documentation, CI/CD, and production readiness.
• Talent Development & Mentorship: Build strong engineering teams through technical mentorship, career development, succession planning, and continuous learning.
• Cross-Functional Alignment: Act as the technical bridge between product leaders, executive stakeholders, clinical advisors, compliance teams, and customer delivery organizations.
• Technical Governance: Drive engineering KPIs, delivery predictability, technical debt management, architectural compliance, and continuous improvement.

2. Healthcare Solution Architecture & Domain Engineering

• EHR & Practice Management Architecture: Define and evolve scalable, multi-tenant EHR and PMS platforms supporting patient registration, scheduling, clinical documentation, care coordination, provider workflows, and practice administration.
• Revenue Cycle Management (RCM): Architect end-to-end revenue cycle workflows covering eligibility verification, charge capture, medical coding, claims submission, remittance processing, denial management, and payment reconciliation.
• Healthcare Interoperability: Lead integrations with EHR/PMS platforms, healthcare clearinghouses, payer systems, and external clinical applications using standards such as HL7 v2, FHIR, and applicable US X12 EDI transactions, including 837, 835, 270/271, and 278.
• Compliance & Data Governance: Incorporate healthcare privacy, security, auditability, consent management, and applicable regulatory requirements into platform architecture, including HIPAA and relevant international frameworks.
• Architecture Artifacts & Governance: Own target-state architecture blueprints, High-Level Design (HLD), Low-Level Design (LLD), Architecture Decision Records (ADRs), reference architectures, and Architecture Review Board processes.
• Domain-Driven Design: Establish clear service boundaries, domain models, API contracts, and integration patterns aligned with clinical, administrative, and financial healthcare workflows.

3. Cloud-Native Architecture, Scalability & Resilience

• Cloud Architecture Strategy: Design and govern cloud-native solutions across AWS, Microsoft Azure, and/or Google Cloud Platform, selecting appropriate services based on business, security, performance, and cost requirements.
• Distributed Systems & Microservices: Lead the design of secure, modular, API-first architectures using microservices or other appropriate architectural patterns.
• High Availability & Disaster Recovery: Define resilience strategies covering fault tolerance, automated failover, backup and recovery, disaster recovery, business continuity, and multi-region deployment where required.
• Data Architecture & Persistence: Guide database selection, data modeling, transaction management, interoperability, event-driven integration, and data lifecycle governance.
• DevSecOps & Continuous Delivery: Establish automated CI/CD pipelines, infrastructure as code, observability, automated testing, progressive delivery, and zero- or minimal-downtime deployment strategies.
• Performance & Cost Optimization: Drive capacity planning, performance engineering, cloud cost optimization, and service-level objectives (SLOs) aligned with business-critical healthcare operations.

4. Agentic AI, LLMs & Intelligent Automation

• AI Architecture & Strategy: Define the architecture and implementation roadmap for LLM-powered applications, AI agents, and intelligent automation across clinical, administrative, and revenue cycle workflows.
• Agentic Workflow Orchestration: Design secure, scalable orchestration patterns for multi-step agent workflows, tool integration, human-in-the-loop approvals, exception handling, and workflow state management.
• Healthcare Process Automation: Identify and implement AI/RPA opportunities in clinical document processing, data extraction, claims operations, prior authorization, denial management, and administrative workflow automation.
• AI Integration & Governance: Establish standards for model selection, retrieval-augmented generation (RAG), context management, structured outputs, model evaluation, observability, and versioning.
• Responsible AI & Human Oversight: Implement safeguards for patient data privacy, access control, traceability, hallucination mitigation, audit logging, and appropriate clinical or operational human review.
• AI Platform Reliability: Define monitoring, evaluation, fallback mechanisms, cost controls, and performance benchmarks to ensure AI-enabled workflows remain reliable and production-ready.

5. Security, Reliability & Operational Excellence

• Security by Design: Embed identity and access management, least-privilege access, encryption, secrets management, secure API design, and threat modeling into architecture and delivery processes.
• Healthcare Data Protection: Establish appropriate controls for protected health information (PHI), data segregation, retention, audit trails, and secure data exchange.
• Production Operations: Define observability standards covering logs, metrics, distributed tracing, alerting, incident response, and root-cause analysis.
• Reliability Engineering: Establish service-level indicators (SLIs), service-level objectives (SLOs), recovery objectives, and resilience testing for critical healthcare services.
• Continuous Improvement: Use production insights, delivery metrics, security findings, and customer feedback to improve platform reliability, engineering productivity, and overall system quality.

Expected Outcomes:

• A clearly defined and executable architectural roadmap aligned with business and product strategy.
• Scalable, secure, and resilient healthcare platforms supporting multi-tenant EHR, PMS, and RCM workflows.
• Improved engineering delivery predictability, team effectiveness, code quality, and operational maturity.
• Interoperable healthcare systems aligned with applicable clinical data and US healthcare transaction standards.
• Production-ready AI and automation capabilities that improve workflow efficiency while maintaining appropriate human oversight.
• Strong architectural governance, reduced technical debt, and sustainable platform evolution.

Leadership Profile:

The successful candidate will combine principal-level architectural depth with hands-on engineering leadership. They will be comfortable making strategic technology decisions, reviewing complex designs, guiding implementation teams, managing delivery trade-offs, and communicating technical risks and opportunities to both business and engineering stakeholders.

Desired profile:

• US Healthcare IT Ecosystem: Deep familiarity with US healthcare standards, data interoperability frameworks (HL7, FHIR, CPT, ICD-10), clearinghouse architectures, and navigating enterprise US insurance payer portals (4,000+ payer networks, clearinghouse connections, and real-time eligibility/prior-authorization integrations).
• Production Agentic AI & LLM Systems: Hands-on experience designing, deploying, and maintaining production-grade AI Agents and multi-agent orchestration frameworks (e.g., LangChain, AutoGen, CrewAI, or custom tool-calling/RAG pipelines) applied to complex document processing, clinical summarization, and automated decision-making.
• Agent Governance & Quality Guardrails: Experience building evaluation frameworks, human-in-the-loop (HITL) audit trails, guardrails, and deterministic fallback logic to ensure compliance, safety, and explainability for AI agents operating on PHI (Protected Health Information).
• Enterprise Automation & Ingestion: Familiarity with browser extension architectures, intelligent document processing (IDP/OCR pipelines for EOB/ERA conversion), enterprise RPA tools, and async event-driven task orchestration