Key Responsibilities
- Design, build, test, deploy, and operate cloud-native backend services, APIs, workflows, and integration capabilities.
- Implement business logic, orchestration services, domain services, event-driven patterns, and service composition required by project workstreams.
- Develop integrations with enterprise systems, third-party platforms, data services, pricing/underwriting capabilities, and AI/agent services.
- Leverage AI coding agents and engineering copilots to accelerate development while maintaining strong review, testing, and production quality practices.
- Create automated unit, integration, contract, regression, and performance tests as part of the delivery lifecycle.
- Contribute to API standards, reusable patterns, golden paths, CI/CD pipelines, infrastructure automation, and service reliability practices.
- Own production readiness, monitoring, incident response, defect remediation, and continuous improvement for delivered services.
- Collaborate with Analysts and Product Owners to ensure requirements, contracts, and acceptance criteria are clear and technically executable.
- Backend development experience with Java, Python, .NET, Node.js, TypeScript, or equivalent enterprise technologies.
- API-first design, microservices, service orchestration, event-driven architecture, and distributed systems patterns.
- AWS cloud services, containers, CI/CD, infrastructure as code, secrets management, and automated deployment practices.
- Experience with contract testing, automated quality gates, performance tuning, observability, and production support.
- Comfortable working in an AI-assisted delivery environment where engineers guide, review, harden, and operationalize agent-generated outputs.
- Design and maintain ontologies, taxonomies, business vocabularies, semantic models, and concept relationships across domains.
- Develop knowledge graphs, entity relationships, metadata structures, and mappings that allow agents and systems to reason across business concepts.
- Engineer AI agent patterns, retrieval-augmented generation pipelines, prompt strategies, orchestration flows, evaluation frameworks, and reusable AI components.
- Generative AI, LLM application development, prompt engineering, agent orchestration, and RAG architecture.
- Ontology modeling, taxonomy development, knowledge graphs, semantic technologies, and metadata design.
- Python, ML/AI frameworks, vector databases, semantic search engines, and data integration patterns.
- Not a narrow coder role — you'll help shape service architecture and own outcomes through production.
- Opportunity to reduce system dependencies through strong contracts and self-service platform patterns.
