AI Architect, Principal Frontline AI Engineer - Healthcare

Key Responsibilities

• Partner directly with healthcare executives, business stakeholders, clinical leaders, and technology teams to identify AI transformation opportunities and define AI-driven solutions.
• Translate complex healthcare business challenges into scalable AI architectures, technical strategies, and implementation roadmaps.
• Design and architect enterprise AI solutions leveraging Generative AI, Large Language Models (LLMs), AI agents, Retrieval-Augmented Generation (RAG), and intelligent automation.
• Define AI solution patterns including multi-agent architectures, workflow orchestration, knowledge retrieval systems, and enterprise AI integrations.
• Lead AI solution development from discovery and architecture through prototyping, production deployment, and optimization.
• Design AI-enabled workflows to transform healthcare operations including claims processing, prior authorization, care management, provider operations, member services, and payment integrity.
• Build and guide implementation of AI applications using modern AI frameworks, cloud platforms, APIs, and enterprise data ecosystems.
• Collaborate with engineering, data science, product, security, and governance teams to ensure scalable, secure, and responsible AI adoption.
• Establish AI architecture standards, best practices, reusable frameworks, and accelerators for enterprise AI delivery.
• Support AI governance practices including model monitoring, explainability, security, compliance, and responsible AI implementation.
• Lead technical workshops, architecture reviews, executive presentations, and solution demonstrations with client stakeholders.
• Mentor engineering teams and enable business teams to adopt AI tools, workflows, and AI-native operating models.

Required Skills & Experience

• 10+ years of experience in software engineering, AI engineering, machine learning, enterprise architecture, or digital transformation.
• Hands-on experience designing and implementing Generative AI and AI/ML solutions in enterprise environments.
• Strong understanding of Large Language Models (LLMs), AI agents, prompt engineering, RAG architectures, and AI application patterns.
• Experience architecting and delivering production-grade AI solutions from concept through deployment.
• Strong software engineering background with proficiency in Python, APIs, microservices, and cloud-based application development.
• Experience designing AI workflows, orchestration patterns, tool integrations, and enterprise AI architectures.
• Experience working with cloud AI platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
• Ability to translate business requirements into technical architectures and communicate solutions to both technical and executive audiences.
• Experience working directly with business stakeholders, clients, or cross-functional teams in a consulting or enterprise environment.
• Experience with AI governance, security, monitoring, evaluation frameworks, and responsible AI practices.
• Healthcare industry experience with understanding of payer/provider workflows, healthcare operations, and regulated environments.
• Knowledge of healthcare compliance considerations including HIPAA and healthcare data privacy.

Good to Have

• Experience as an AI Architect, Forward Deployed Engineer, Solutions Architect, or Consulting Architect.
• Experience building agentic AI systems using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or Semantic Kernel.
• Experience with Claude (Anthropic), OpenAI, Gemini, or other enterprise LLM platforms.
• Experience developing healthcare AI solutions for: Claims processing and adjudication, Prior authorization, Utilization management, Care management, Member/provider engagement, Revenue cycle management, Payment integrity.
• Familiarity with healthcare data standards including HL7, FHIR, ICD-10, CPT, and claims data structures.
• Experience with Retrieval-Augmented Generation (RAG), vector databases, embeddings, and enterprise search solutions.
• Experience integrating AI solutions with enterprise platforms such as Salesforce, ServiceNow, Epic, or healthcare workflow systems.
• Experience creating reusable AI accelerators, reference architectures, and enterprise AI frameworks.
• Experience supporting AI adoption, enablement, and training for business and engineering teams.
• Experience working in highly regulated industries where security, governance, and auditability are required.