AI-Powered Backend Engineer for Enterprise Insurance

Herald; New York (NY); Hybride; USD 140 000 - 200 000; Plein temps

Avantages offerts par ce poste

Hybrid work in Northeast

Relocation stipend

Health, dental, vision insurance

401K plan

Home office stipend

Résumé du poste

Herald in New York is seeking an experienced Software Engineer to join the Babbix product team. You will design, build, and scale backend systems, data pipelines, and model orchestration to deliver AI-driven workflows for brokers.

You’ll work with product and client services to turn ideas into production features, ensuring transparency, accuracy, and reliability in the AI output while improving internal tooling and deployment processes.

Qualifications

• 2+ years building backend or full-stack systems from design to production.
• Fluent in at least one modern language (Python, TS, Go, Rust) and quick to adapt.
• Hands-on with LLMs and vector databases or similar AI tech.
• Experience with relational or NoSQL databases (Postgres, MySQL, MongoDB, Redis).
• Experience with Docker and major cloud providers (GCP, AWS, Azure).
• Experience shaping products at fast-growing startups.

Responsabilités

• Build core AI-product features for enterprise brokers across APIs and data pipelines.
• Improve developer experience, tooling, testing infrastructure, and CI/CD.
• Collaborate with clients and cross-functional teams to gather feedback and translate it into features.

Connaissances

Backend systems design

Programming languages

AI/LLMs familiarity

Databases (Postgres/MySQL/MongoDB/0)

Cloud platforms (AWS/GCP/Azure)

Startup product experience

Outils

Docker

Cloud providers

Description du poste

Herald in New York is seeking an experienced Software Engineer to join the Babbix product team. You will design, build, and scale backend systems, data pipelines, and model orchestration to deliver AI-driven workflows for brokers.

You’ll work with product and client services to turn ideas into production features, ensuring transparency, accuracy, and reliability in the AI output while improving internal tooling and deployment processes.