Platform Engineering Manager, AI Submission Ingestion

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 release
• 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 clearly to both engineering teams and executive stakeholders

Chubb is a world leader in insurance. With operations in 54 countries, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance, and life insurance to a diverse group of clients. The company is distinguished by its extensive product and service offerings, broad distribution capabilities, exceptional financial strength, underwriting excellence, superior claims handling expertise and local operations globally.

At Chubb, we are committed to equal employment opportunity and compliance with all laws and regulations pertaining to it. Our policy is to provide employment, training, compensation, promotion, and other conditions or opportunities of employment, without regard to race, color, religious creed, sex, gender, gender identity, gender expression, sexual orientation, marital status, national origin, ancestry, mental and physical disability, medical condition, genetic information, military and veteran status, age, and pregnancy or any other characteristic protected by law. Performance and qualifications are the only basis upon which we hire, assign, promote, compensate, develop and retain employees. Chubb prohibits all unlawful discrimination, harassment and retaliation against any individual who reports discrimination or harassment.

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

The pay range for the role is $150,000 to $205,000. The specific offer will depend on an applicant's skills and other factors. This role may also be eligible to participate in a discretionary annual incentive program. Chubb offers a comprehensive benefits package, more details on which can be found on our careers website. The disclosed pay range estimate may be adjusted for the applicable geographic differential for the location in which the position is filled.