AI Solutions Architect - Healthcare

Lead the design and delivery of AI solutions for healthcare clients, focusing on agentic systems and LLM-enabled applications. This involves architecting solutions using RAG, embeddings, vector databases, and orchestration frameworks, and governing LLMOps practices like evaluation, monitoring, and guardrails.

What you'd actually do

  1. Lead small engagements or key workstreams within large, complex data and analytics transformations for Healthcare clients
  2. Design and implement cloud and hybrid data architectures, migration approaches, and technology solutions across data and analytics platforms
  3. Manage day-to-day stakeholder relationships and align technical delivery to business and program objectives
  4. Oversee quality of team deliverables and recommendations while contributing to hands-on solution delivery across distributed teams
  5. Support practice growth through proposal development, thought leadership, and coaching junior practitioners

Skills

Required

  • AI solutions for Healthcare organizations
  • AI/GenAI solution architecture
  • agentic systems
  • LLM-enabled applications
  • RAG
  • embeddings
  • vector databases
  • orchestration frameworks
  • memory/state patterns
  • tool-use architectures
  • LLMOps
  • evaluation
  • monitoring
  • prompt lifecycle management
  • guardrails
  • leading complex AI Solutions engagements
  • cloud and hybrid data architectures
  • migration approaches
  • technology solutions across data and analytics platforms

Nice to have

  • Healthcare Payer/Provider AI Solutions experience
  • consulting and client-facing delivery roles
  • creating critical collaterals for client workshops and customer interactive sessions
  • presenting technical content to both large and small audiences

What the JD emphasized

  • 10+ years hands-on experience delivering AI solutions for Healthcare organizations
  • 2+ years hands-on experience with AI/GenAI solution architecture, especially agentic systems and LLM-enabled applications
  • 2+ years experience with RAG, embeddings, vector databases, orchestration frameworks, memory/state patterns, and tool-use architectures
  • 2+ years experience building or governing LLMOps practices such as evaluation, monitoring, prompt lifecycle management, and guardrails
  • 4+ years experience leading, managing and delivering complex AI Solutions engagements

Other signals

  • AI/GenAI solution architecture
  • agentic systems
  • LLM-enabled applications
  • RAG
  • embeddings
  • vector databases
  • orchestration frameworks
  • LLMOps
  • evaluation
  • prompt lifecycle management
  • guardrails