Senior Consultant AI/ML

• 10+ years in technology delivery with 5+ years leading AI/ML programs at enterprise scale to serve as the Subject Matter Expert for the Agentic AI workstream. This resource will lead customer-facing conversations to understand use cases, design agentic AI solutions, and drive implementation using AWS Bedrock AgentCore and supporting services. The consultant must combine deep AI/ML engineering expertise with the ability to act as a trusted technical advisor to customer stakeholders. The consultant will own end-to-end delivery of an enterprise-scale AI/ML solution that fully automates the healthcare claims processing lifecycles from claims intake and adjudication through payment and denial management. This role requires a blend of technical depth in AI/ML, healthcare domain expertise, and strong program leadership to drive transformation at scale.
• Define and own the AI/ML solution roadmap for end-to-end claims automation, aligning with the customer’s enterprise transformation goals
• Work with cross-functional teams (data scientists, AI/ML engineers, cloud architects, business analysts, and SMEs) to deliver production-grade AI/ML solutions
• Serve as the primary point of accountability for project delivery, risk management, and stakeholder communication
• Architect the AI/ML pipeline: claims intake , data extraction, eligibility verification, medical coding validation, auto-adjudication, fraud detection, and payment processing
• Oversee model development, training, validation, and deployment across the claims lifecycle
• Ensure solution scalability to handle enterprise-level claim volumes (millions of claims/month)
• Define AI/MLOps strategy including model monitoring, retraining pipelines, drift detection, and testing
• Lead technical workshops with customer to identify, prioritize, and scope agentic AI use cases
• Design agentic patterns (ReAct, tool-use, multi-agent orchestration) mapped to customer business problems
• Build and operate evaluation pipelines for agentic systems (correctness, safety, latency, cost)
• Implement agents using Strands SDK (or equivalent) with Bedrock AgentCore Runtime
• Design and implement RAG architectures using Bedrock Knowledge Bases and vector stores
• Instrument agents for tracing, monitoring, and debugging via Bedrock AgentCore Observability

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