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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading applied AI and health system engineering projects, translating clinical operations needs into AI solutions, and managing healthcare AI implementations. Proficient in MLOps practices and familiar with major AI platforms in a regulated environment.
Highest-signal resume keywords
Applied AI Solution DevelopmentMLOps Platform ExperienceHealthcare AI ImplementationData Warehouse TechnologyClinical Workflow Translation
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI Solution RequirementsData ModelingBenchmark TestingRAG Pipeline DevelopmentAgentic AI WorkflowsModel Governance PracticesAnalytical TechniquesPopulation Health Program DesignAI FluencySoftware Implementation
Soft Skills
Analytical ThinkingCoaching TeamsCommunication SkillsProblem SolvingTechnical Decision Making
Tools & Technologies
Azure OpenAI ServiceAWS BedrockGoogle Vertex AISnowflakeDatabricks
Certifications & Qualifications
Bachelor's Degree
Industry Keywords
Health System EngineeringGenAI Use CasesPHI HandlingHIPAA ComplianceHealthcare AI Solutions
Tech Stack
Tools & technologiesAWSAzureCloudSnowflake
About the role
Key responsibilities & impact- Lead client-facing applied AI and health system engineering work within PwC's Advisory practice
- Lead applied AI and health system engineering workstreams from design through deployment for client engagements
- Translate health services and clinical operations needs into AI solution requirements, reference architectures, and delivery roadmaps
- Design retrieval, memory, prompt, and agent orchestration components for scalable production model usage
- Guide data wrangling, model integration, and software implementation across cloud-based AI platforms
- Evaluate model behavior through benchmark testing, human review, and scoring methods
- Advise clients on platform selection, governance, portability, and workload fit across major AI platforms and integrations
- Coach teams through complex delivery challenges, resolve blockers, and elevate technical decision making
- Communicate technical tradeoffs, system dependencies, and delivery status to client stakeholders and internal leaders
- Develop LLMOps and AgentOps practices to improve observability, traceability, and iterative model refinement
- Apply analytical thinking, AI fluency, and data modeling to identify automation and cognitive solution opportunities
Requirements
What you’ll need- At least a Bachelor's degree
- At least 7 years of experience
- Demonstrated experience delivering production AI or GenAI use cases in a health system environment
- Hands-on experience building RAG pipelines or agentic AI workflows against clinical data sources
- Experience with MLOps platforms and model governance practices in regulated, PHI-handling environments
- Ability to translate clinical and revenue cycle workflows into structured AI use case requirements
- Familiarity with Azure OpenAI Service, AWS Bedrock, or Google Vertex AI in a HIPAA-compliant deployment context
- Understanding of population health program design and how AI accelerates outcomes
- Proven record of success managing healthcare AI and GenAI solutions
- Experience in analytical techniques applied to healthcare use cases
- Experience working with data warehouse technology including Snowflake or Databricks
- Willingness to travel up to 80%
- Must not require, now or in the future, PwC sponsorship through the H-1B lottery, except as set forth in PwC's policy
Benefits
Comp & perks- Annual discretionary bonus
- Medical insurance
- Dental insurance
- Vision insurance
- 401k
- Holiday pay
- Vacation
- Personal and family sick leave