Remote AI & ML Solution Architect for Healthcare

NTT DATA, Inc.

Plano (TX)

On-site

USD 119,578 - 221,625

Full time

14 days+

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Job summary

NTT DATA Services in Plano, Texas is seeking an experienced AI Solutions Architect to design and deliver AI, ML, and generative AI solutions for healthcare systems, hospitals, and provider networks. You will partner with clinical, operational, financial, digital, data and technology leaders to translate provider challenges into scalable AI-enabled solutions.

The role requires strong knowledge of EHR ecosystems, healthcare data standards, cloud architecture, analytics platforms, and responsible

Qualifications

Bachelor’s degree in computer science, information systems, engineering, data science, healthcare informatics, clinical informatics, or a related field.
8+ years of experience in technology architecture, data architecture, analytics, AI/ML, enterprise solution delivery, or healthcare technology consulting.
4+ years of experience supporting healthcare providers, health systems, hospitals, academic medical centers, physician groups, or integrated delivery networks.
Hands‑on experience designing AI, machine learning, generative AI, analytics, or automation solutions in production environments.
Strong understanding of provider business and clinical processes, including patient access, EHR workflows, clinical documentation, care delivery, revenue cycle, quality reporting, population health, and patient engagement.
Experience with healthcare data standards and regulations, such as HIPAA, FHIR, HL7, CDA, X12, USCDI, interoperability requirements, and clinical data exchange.
Experience with major cloud platforms such as AWS, Azure, or Google Cloud, including data, AI, security, integration, and governance services.
Familiarity with modern AI patterns such as RAG, vector databases, prompt engineering, model evaluation, MLOps, LLMOps, clinical validation, and AI governance.
Ability to communicate complex technical concepts to executive, clinical, operational, financial, and technical audiences.
Strong consulting, facilitation, problem‑solving, stakeholder management, and executive presentation skills.

Responsibilities

Design end‑to‑end AI and generative AI solutions for provider use cases, including architecture, data flows, integration patterns, model selection, workflow integration, governance, security, and deployment approach.
Partner with healthcare system stakeholders to identify high‑value AI opportunities, define business outcomes, assess feasibility, and shape implementation roadmaps.
Lead solutioning for provider‑specific domains such as electronic health records, clinical documentation, patient access, scheduling, revenue cycle, coding, billing, denials management, care coordination, population health, quality reporting, command centers, and patient engagement.
Develop reference architectures using cloud platforms, EHR integration services, data lakes, warehouses, lakehouses, MLOps pipelines, APIs, interoperability layers, workflow tools, and enterprise integration patterns.
Guide teams on applying large language models, retrieval‑augmented generation, predictive analytics, natural language processing, computer vision, intelligent automation, and decision support in healthcare delivery settings.
Collaborate with data scientists, engineers, clinicians, informaticists, compliance teams, security architects, revenue cycle leaders, and delivery teams to move concepts from prototype to production.
Define responsible AI controls, including model monitoring, explainability, clinical safety review, human‑in‑the‑loop validation, bias assessment, auditability, privacy, cybersecurity, and regulatory compliance.
Support pre‑sales and client advisory activities, including solution presentations, proposals, estimates, demos, workshops, executive briefings, and innovation strategy sessions.
Evaluate vendor platforms and AI tools across EHR ecosystems, cloud, clinical documentation, revenue cycle, patient engagement, workflow automation, and analytics.
Create reusable assets, including provider AI solution blueprints, architecture patterns, implementation playbooks, data requirements, governance frameworks, and value case templates.