Software Engineer

Software Engineer, AI-Enabled Services (GCP)Role SummaryWe are seeking a Software Engineer to build and ship production-ready AI-enabled services on Google Cloud Platform (GCP). Our agents and workflows are assembled in a low-code agent builder and n8n. This role focuses on the high-code layer underneath: MCP servers, APIs, and application services that give those agents and workflows secure, reliable access to enterprise systems and data.The ideal candidate combines strong software engineering fundamentals with a rigorous approach to quality and the ability to work directly with partners. You will translate business needs from technical and non-technical stakeholders into well-designed, well-tested services, and you will hold a high bar for what “production-ready” means.Key Responsibilities

• Design, build, and maintain high-code services on GCP, including MCP servers, APIs, and supporting application services, using Cloud Run, GKE, Pub/Sub, Cloud Functions, and related capabilities.
• Build integrations with enterprise systems, APIs, databases, and event streams that low-code agents and n8n workflows can use reliably.
• Own quality for the products you build: define test strategies, write automated unit, integration, and end-to-end tests, and establish release-readiness criteria.
• Validate the behavior of AI-enabled workflows, including tool invocation, error handling, edge cases, and regressions, and help define evaluation approaches for non-deterministic outputs.
• Design, develop, and manage relational database schemas, queries, migrations, and performance tuning in support of application and workflow needs.
• Meet with partners, both technical and non-technical, to gather requirements, clarify goals, and walk through designs.
• Translate their needs into clear technical specifications, and explain trade-offs, risks, and status in language each audience understands.
• Collaborate with architects, product managers, and domain experts to shape scalable solutions and set expectations on scope, timelines, and quality.
• Help define patterns and standards for service design, observability, testing, security, and operational support across our low-code and high-code environments.
• Implement strong engineering practices, including CI/CD, code reviews, monitoring, alerting, and infrastructure-as-code.
• Optimize solutions for performance, resilience, cost, and maintainability.
• Partner with security and platform teams to ensure compliant and secure handling of data, credentials, and enterprise integrations.

Required Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience.
• Proven experience as a software engineer building and supporting production-grade applications and services.
• Strong programming skills in one or more modern languages such as Python, TypeScript, or Java.
• Hands-on experience building and deploying services on Google Cloud Platform.
• Demonstrated QA discipline: experience with test automation, test planning, API and integration testing, and release validation for production systems.
• Strong experience with relational database development and management, including schema design, SQL, indexing, query optimization, and migrations.
• Experience developing and consuming REST APIs and working with event-driven architectures and distributed systems.
• Familiarity with containerization and deployment technologies such as Docker and Kubernetes.
• Excellent communication skills, with a track record of gathering requirements and discussing design with both technical and non-technical partners, and of presenting technical concepts clearly to varied audiences.
• Ability to work across technical and functional teams in a fast-moving environment.

Preferred Experience

• Experience building or consuming MCP servers or similar tool-integration layers for AI agents.
• Experience with low-code or workflow automation platforms such as n8n, and an understanding of how to build services that support them.
• Experience with NoSQL databases, vector stores, and analytical platforms.
• Exposure to AI-native applications, such as LLM-based workflows, retrieval-augmented generation, or tool use, including methods for evaluating and testing them.
• Familiarity with enterprise integration patterns, middleware, and event streaming.
• Experience designing systems with observability, traceability, and human-in-the…