Platform Engineering Manager, AI Submission Ingestion

JOB DESCRIPTION

As the Technical Platform Lead for our AI Submission Ingestion Platform, you will define and drive the end-to-end technical vision for how commercial insurance submissions move from inbox to underwriting decision.

This role requires deep engineering expertise, with responsibility for shaping the platform's technical direction, data flows, integration patterns, AI capabilities, and release readiness in close partnership with engineering, architecture, data science, and governance teams.

You will own the technical roadmap for your area within the broader platform strategy, making informed trade-offs across scalability, reliability, data quality, model performance, and delivery sequencing.

Rather than acting as a traditional product manager, you will operate as a technical leader who can translate business needs into platform capabilities, challenge design decisions, establish technical standards, and ensure the solution is built for long-term reuse, resilience, and operational excellence.

In the US, you will partner closely with underwriting, operations, and regional business leaders to understand workflow needs and convert them into technical requirements that engineering squads can execute.

You will also work with peer leaders in the US and India, along with business-unit and regional technology teams, global platform engineering squads, architecture, data science, and risk and governance teams, to ensure the platform is designed and delivered with both business outcomes and technical integrity in mind.

This is a high-impact role for a technical leader who has built and scaled platforms, understands AI/ML system design, and can establish a clear end-to-end vision for a platform that reduces manual work, improves decision quality, and creates durable capabilities the business can trust.

In this role, you will:


* Lead technical discovery with underwriters, operations leaders, and business partners to assess submission intake needs, platform readiness, and technical feasibility


* Define the end-to-end technical vision and roadmap for your platform area, including architecture, data flows, integrations, AI evaluation, and operational readiness


* Make technical trade-offs across performance, scalability, maintainability, data quality, and delivery sequencing, and clearly explain recommendations to engineering and business stakeholders


* Translate business needs into technical requirements, non-functional requirements, acceptance criteria, and release gates


* Establish platform standards for APIs, event flows, data contracts, observability, and integration patterns


* Define and track outcome measures such as straight-through processing rate, manual-review volume, time to onboard a product, turnaround time, model precision/recall, and operational defect rates


* Set the AI quality bar, including confidence thresholds, human-review rules, drift monitoring, and evaluation criteria required for releas...