Applied AI Health System Engineer [Health Services-Payer] – Senior Manager

PwC•Tampa, FL

5d•$124,000 - $280,000

About The Position

As an Applied AI Health System Engineer in PwC's Advisory practice, you will lead client-facing AI engineering work for health sector use cases, shaping solution design, implementation, and deployment. As a Senior Manager, you will guide large, complex efforts, use sound judgment in ambiguous situations, coach teams through delivery challenges, and translate technical direction into practical action for clients and internal stakeholders. You will apply data, algorithms, and software engineering to build AI and machine learning solutions at scale, design system architecture, support model readiness, and drive adoption of AI-enabled capabilities across the engagement lifecycle.

Requirements

At least a Bachelor's degree.
At least 7 years of experience.

Nice To Haves

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.

Responsibilities

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 that support scalable model usage across production environments.
Guide data wrangling, model integration, and software implementation activities across cloud-based AI platforms.
Evaluate model behavior through benchmark testing, human review, and scoring methods to validate performance and reliability.
Advise clients on platform selection, governance considerations, portability, and workload fit across OpenAI, Anthropic, Google Gemini, Amazon Bedrock, Snowflake Cortex, Databricks Mosaic AI, and MCP integrations.
Coach teams through complex delivery challenges, resolve blockers, and elevate technical decision making across multiple workstreams.
Communicate technical tradeoffs, system dependencies, and delivery status to client stakeholders and internal leaders.
Develop LLMOps and AgentOps practices that improve observability, traceability, and iterative model refinement after launch.
Apply analytical thinking, AI fluency, and data modeling skills to identify opportunities for automation and cognitive solution adoption.