Staff Backend Engineer

What you will do

• Design, build, and operate backend services for insurance data ingestion, processing, decisioning, and workflow automation.

• Model complex, messy insurance data in PostgreSQL and evolve schemas without disrupting production systems.

• Build asynchronous, event-driven workflows for long-running processes such as document processing, claims evaluation, and decision pipelines.

• Develop data pipelines that make source data complete, traceable, and usable for downstream AI systems.

• Build evaluation and observability loops that measure decision quality, correctness, latency, cost, and failure modes.

• Integrate LLM-based components into production systems with clear boundaries, fallbacks, monitoring, and evaluation.

• Improve reliability, performance, and cost efficiency across cloud infrastructure and serverless workloads.

• Lead technical design reviews, unblock delivery, and raise risks early with product, AI, and engineering stakeholders.

• Take end-to-end ownership of projects: problem definition, design, implementation, rollout, monitoring, and iteration.

Technical requirements

• 8+ years of professional backend or full-stack software engineering experience.

• Strong Python experience in production systems; strong engineers from Java, C#, Go, or similar object-oriented backgrounds will also be considered.

• Solid relational database skills, preferably PostgreSQL: schema design, indexing, query optimization, migrations, transactions, and data integrity.

• Hands-on experience with AWS or a comparable cloud provider, including serverless compute such as Lambda and/or EC2-based services.

• Experience designing asynchronous systems: queues, background jobs, retries, idempotency, dead-letter handling, and workflow orchestration.

• Strong system-design fundamentals: APIs, service boundaries, data contracts, caching, consistency, failure handling, and scalability.

• Strong debugging and production-operations skills: logging, tracing, metrics, alerting, incident response, and root-cause analysis.

• Experience building or maintaining data pipelines, ETL/ELT systems, or data-intensive backend services.

• Ability to write well-tested, maintainable code and improve engineering standards across a team.

Strong plus

• Experience shipping LLM or generative-AI features in production, including prompt workflows, retrieval, evaluation, guardrails, or human-in-the-loop review.

• Distributed systems experience: event-driven architecture, concurrency, distributed transactions, consistency trade-offs, or multi-service coordination.

• Experience in insurance, fintech, healthcare, legal tech, or another domain with regulated, document-heavy, high-value data.

• Experience with workflow orchestration, document processing, OCR, or decision-support systems.

• Experience defining technical strategy for a small, high-ownership engineering team.