Remote Opportunity || Enterprise Architect with AI, Cloud and Data with Healthcare Payer || Contract

Job Title : Enterprise Architect with AI, Cloud and Data with Healthcare Payer

Location: Newark, NJ Remote

Mode of Hire: Contract

Job Description:

Must have experience in AI, Cloud and Data- ( we can look for enterprise level cloud architect or AI architect or technical architect).

Must have strong in healthcare payer experience

Role Overview:

The Enterprise Architect (Consultant) serves as a strategic, enterprise - level advisor responsible for shaping and governing the enterprise technology landscape in alignment with business strategy. This role focuses on capability alignment, standards, roadmaps, and architectural guardrails across domains rather than designing individual application or solution implementations.

The consultant will operate within the client's Enterprise Architecture function, influencing investment decisions, modernization priorities, and technology governance-while enabling consistent adoption of cloud, data, AI, and GenAI capabilities across the enterprise.

Required Qualifications:

Experience:

• 5+ years of experience in Enterprise Architecture or enterprise-level consulting roles .
• Proven experience defining enterprise target states and domain/ capability roadmaps , independent of individual solutions.
• Experience operating in large, complex, regulated enterprises .
• Healthcare, payer, insurance, or similarly regulated industry experience preferred.

Enterprise Architecture Skills:

• Strong grounding in Enterprise Architecture frameworks and practices (e.g., TOGAF).
• Experience working with capability models, reference architectures, and portfolio views .
• Ability to distinguish enterprise-level architecture decisions from solution or project-level design.

Technical Knowledge (Enterprise Context):

• Broad understanding (not deep engineering ownership) of:
  • Cloud platforms (AWS, Azure)
  • Enterprise integration and API ecosystems
  • Data platforms and analytics
  • Automation and AI-enabling platforms
• Familiarity with security-by-design and enterprise risk considerations.
• Comfortable working with Agile/DevOps organizations without owning delivery mechanics.

AI & Data (Enterprise-Level Expectations):

• Working knowledge of AI and GenAI concepts as they apply to enterprise platforms, governance, and reuse .
• Ability to evaluate AI solutions based on enterprise readiness , not demo success.
• Familiarity with data governance, model lifecycle considerations, and regulatory implications.

Consulting & Communication:

• Strong executive communication and facilitation skills.
• Ability to frame architecture in terms of business outcomes, risk, and long-term value .
• Comfortable operating in advisory roles without direct delivery ownership.
• Self-directed, structured, and outcome-focused.

Certifications (Preferred):

• TOGAF or equivalent Enterprise Architecture certification.
• AI, data, or analytics certifications are a plus.

Enterprise Architecture Responsibilities:

Enterprise Strategy & Capability Alignment:

• Partner with business and IT leadership to define and evolve enterprise technology strategy and multi - year roadmaps .
• Align technology direction to business capabilities, operating models, and investment themes , rather than individual projects.
• Identify enterprise-wide gaps, redundancies, and modernization opportunities across applications, platforms, and vendors.
• Guide build vs. buy vs. rationalize decisions from an enterprise portfolio perspective.

Target - State & Reference Architecture:

• Define and maintain enterprise target-state architectures , transition states, and reference architectures.
• Establish and evolve architecture principles, standards, and patterns covering:
  • Cloud and hybrid platforms
  • Integration and interoperability
  • Enterprise data and analytics platforms
  • AI, GenAI, and automation capabilities
• Ensure consistency and reuse across domains instead of point-solution optimization.
• Provide architectural guardrails that delivery and solution teams must align to (but do not own solution design).

• Stakeholder management - Influence without authority through structured trade-off analysis, principles, and evidence-based recommendations .

AI, GenAI & Data at the Enterprise Level:

• Support evolution of enterprise AI and GenAI architecture patterns , including:
  • Shared AI platforms and services
  • RAG and knowledge enablement architectures
  • Predictive analytics and decisioning capabilities
• Guide enterprise adoption of AI with focus on:
  • Responsible and ethical AI
  • Explainability and auditability
  • Security, privacy, and PHI protection
• Support AI use - case intake and prioritization from a portfolio and value perspective.
• Prevent fragmented or duplicative AI implementations across business units.

Architecture Governance:

• Participate in and support Enterprise Architecture governance structures , including Architecture Review Boards.
• Enforce adherence to enterprise standards, principles, and roadmaps .
• Help define and refine:
  • Technology standards and guardrails
  • Exception and risk management processes
  • Technology lifecycle and obsolescence management
• Ensure outputs are decision - oriented, traceable, and audit - ready .