Platform Engineering Manager, AI Submission Ingestion

Chubb•Jersey City, NJ

11d•$150,000 - $205,000

About The Position

As the Technical Platform Lead for the AI Submission Ingestion Platform, this role defines and drives the end-to-end technical vision for moving commercial insurance submissions from inbox to underwriting decision. The role shapes the platform’s technical direction, data flows, integration patterns, AI capabilities, and release readiness in partnership with engineering, architecture, data science, and governance teams. It owns the technical roadmap for its platform area within the broader platform strategy and makes informed trade-offs across scalability, reliability, data quality, model performance, and delivery sequencing. This is a technical leadership role rather than a traditional product-management role. The successful candidate translates business needs into platform capabilities, challenges design decisions, establishes technical standards, and ensures the platform is built for reuse, resilience, and operational excellence. In the US, the role partners with underwriting, operations, and regional business leaders to understand workflow needs and develop technical requirements for engineering squads. It also works with peer leaders in the US and India, business-unit and regional technology teams, global platform engineering squads, architecture, data science, and risk and governance teams. The platform aims to reduce manual work, improve decision quality, and provide durable capabilities the business can trust.

Requirements

At least 7 years of experience in engineering, technical product, platform, or architecture roles, including experience leading complex platform or AI-enabled solutions.
Strong background in software engineering, platform architecture, or technical delivery, with demonstrated ability to shape end-to-end technical solutions.
Bachelor’s degree in engineering, computer science, or a related technical field, or equivalent experience.
Proven ability to define technical strategy, influence senior stakeholders, and make sound trade-offs using data and engineering judgment.
Hands-on experience with AI/ML-enabled products, including evaluation design, confidence thresholds, drift, and human-in-the-loop workflows.
Strong fluency across APIs, event-driven architectures, data models, and integration patterns.
Ability to analyze data independently using SQL or similar tools.
Experience working with cloud platforms, especially Azure, plus workflow orchestration and event streaming.
Clear, concise writing skills that can move decisions for both engineers and executives.

Nice To Haves

Commercial P&C insurance experience, including submission intake, clearance, underwriting workbenches, rating, or policy administration.
Experience with intelligent document processing, extraction platforms, ontologies, knowledge graphs, or canonical data models.
Prior hands-on experience as an engineer, architect, or data scientist is strongly preferred.
Agile delivery experience in a regulated enterprise.

Responsibilities

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 the 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 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 outcomes such as straight-through processing rate, manual-review volume, time to onboard a product, turnaround time, model precision and recall, and operational defect rates.
Set AI quality standards, including confidence thresholds, human-review rules, drift monitoring, and release evaluation criteria.
Convert one-off line-of-business requests into reusable, configurable platform capabilities.
Coordinate rollout across regions and ensure North America requirements are represented in platform decisions.
Validate key functionality in UAT and production, prioritize regression scope by business and technical risk, and ensure readiness for production support.
Partner with risk, governance, privacy, continuity, and disaster-recovery teams to ensure the platform meets enterprise controls.
Communicate release impacts, technical decisions, and business value to engineering teams and executive stakeholders.