84.51° is looking for a Lead Software Engineer to lead the design and development of their public API and supporting services, aiming to build a reliable, secure, and cloud-native platform that is highly available, resilient, and scalable.
Requirements
- 2+ years designing, building, and operating public APIs at scale.
- 3+ years knowledge of cloud services (Azure preferred) and experience selecting the right tools for a given problem.
- 3+ years' experience with API Gateways (Kong preferred), serverless architectures, and container orchestration (AKS/Kubernetes).
- Hands-on experience with event-driven architectures and message queuing systems.
- Strong understanding of API testing methodologies (contract, integration, load testing).
- Deep knowledge of CI/CD practices including deployment strategies and feature flagging.
- Familiarity with OAuth2.0, PKCE, and other authentication models for server-to-server and client-server communication.
Responsibilities
- Design, develop, and maintain a public-facing API that serves as the main entry point for clients.
- Define and implement API versioning strategies, migration patterns, and deprecation policies.
- Lead adoption of testing best practices including contract testing, load testing, and integration testing.
- Build and optimize CI/CD pipelines, ensuring capabilities such as blue/green deployments, feature flagging, canary releases, and rollback plans.
- Architect and implement services leveraging Azure cloud-native technologies (Functions, Service Bus, Event Grid, AKS, Storage, Monitoring).
- Design and build event-driven systems and asynchronous processing patterns.
- Ensure high availability, fault tolerance, and observability across systems.
Other
- 5+ years of professional software engineering experience with a strong focus on backend development
- Mentor engineers, drive architectural discussions, and champion best practices.
- Excellent problem-solving, communication, and collaboration skills.
- Background or experience working with AI/ML systems, whether applying models in production workflows or collaborating with data science teams.
- Familiarity with Model Context Protocol (MCP) or similar emerging standards for integrating AI/ML models into APIs and enterprise systems.
