AI Lead-US Healthcare

Principal AI Product Lead Healthcare Payment Accuracy & Decision Intelligence

Location: Hyderabad

About Us

Rialtic and Exponential AI recently merged to create a unified platform spanning the full healthcare claims lifecycle — combining Rialtic’s rules-based payment accuracy platform with Exponential AI’s Enso platform of prebuilt Decision Agents. Headquartered in Atlanta, the combined company serves leading U.S. health plans and provider systems, with a dedicated R&D center in Hyderabad, India.

Our mission is to help payers and providers make healthcare meaningfully more affordable for American families and employers. We do this by embedding agentic AI systems — intelligent decision agents — directly into claims and clinical workflows to automate reviews, identify payment issues, and support real-time decision-making, while delivering transparent, predictable, and explainable machine decisions in real time.

About the Role

We’re looking for a Principal AI Product Lead to drive product strategy and execution for our unified payment accuracy and decision intelligence platform. You’ll own one or more flagship areas across our product suite — spanning claims adjudication, coordination of benefits, claims audit, payment integrity, contract management, and utilization management — and shape how AI Decision Agents are productized, scaled, and adopted by the largest payers and provider systems in the country.

This is a senior individual contributor role with executive visibility. You’ll be a critical voice in defining how the combined Rialtic + Exponential AI roadmap evolves post-merger, with the autonomy to set direction and the responsibility to deliver measurable client outcomes.

What You’ll Do

• Own end-to-end product strategy and roadmap for AI-powered offerings across the claim lifecycle, from prepay editing to real-time decision intelligence during and after adjudication.
• Productize and scale Decision Agents on the Enso platform — defining which agents to build, how they compose into client-facing solutions, and how they integrate with Rialtic’s editing system and customers’ legacy infrastructure.
• Translate complex payer and provider problems — payment integrity leakage, claims rework, prior auth bottlenecks, contract compliance, audit defensibility — into well-scoped, high-ROI AI product opportunities.
• Partner closely with AL and ML engineering, data science, and platform teams to define agent requirements, evaluation criteria, and acceptable accuracy and explainability thresholds for production deployment.
• Engage with health plan executives, provider finance executives, payment integrity leaders, and clinical operations teams to validate problems, co-design solutions, and drive adoption.
• Establish frameworks for responsible and explainable AI in claims and clinical decisioning — bias auditing, human-in-the-loop review, audit trails, and continuous learning from feedback loops.
• Define success metrics that matter to clients: dollars saved, leakage prevented, cycle time reduced, auto-adjudication rates, false positive rates, and provider abrasion.
• Serve as a thought partner to the leadership on portfolio strategy, M&A integration priorities, and category positioning in a rapidly consolidating market.
• Mentor product managers, engineers across the combined organization and help shape product culture during this pivotal post-merger phase.

What You Bring

• 10–15 years of product management experience, with a substantial portion building enterprise products in the U.S. healthcare payer and/or provider markets.
• Expertise in at least one of: payment integrity, claims editing and adjudication, payment accuracy, utilization management, revenue cycle management, contract management, or healthcare audit.
• Proven track record of shipping AI/ML-powered products at enterprise scale — ideally including agentic AI, decision intelligence, or workflow automation deployed at health plans or large provider systems.
• Direct experience with Decision Agents, agentic AI architectures, or LLM-powered enterprise workflows in healthcare administration.
• Technical literacy to engage substantively with AIML and platform engineers on agent architecture, model evaluation, real-time inference at scale, integration with legacy core admin systems, and trade-offs between deterministic rules and learned models.
• Strong fluency in healthcare regulation and compliance: HIPAA, state-level payer regulat…