What You’ll Do

● Design, develop, and maintain scalable APIs and backend services (.NET / .NET Core, AWS).

● Build and manage data pipelines and integrations with AI systems and external platforms.

● Work with modern infrastructure and data tools such as Docker, Kubernetes, Kafka, OpenSearch, and Redis.

● Implement strong CI/CD, monitoring, and deployment practices for reliability and repeatability.

● Collaborate closely with AI, QA, and product teams to translate business and research needs into technical solutions.

● Balance speed and scalability — deliver fast, but design for growth.

● Contribute to architecture discussions and help evolve our backend stack for long-term success. What Success Looks Like (6–12 Months)

● Production-grade, scalable backend components supporting real-time AI-driven features.

● Clean, maintainable, and well-tested codebases deployed with strong observability.

● Effective collaboration with AI and QA teams — fast iteration, shared ownership, and clear delivery rhythm.

● Meaningful contribution to engineering culture — high accountability, continuous improvement, and curiosity.

What You Bring

● 6+ years of experience in backend engineering in .NET / .NET Core and AWS environments.

● Proven experience building APIs, distributed systems, and data pipelines in production.

● Solid understanding of system design, scalability, reliability, and performance optimization.

● Familiarity with modern cloud and data technologies (Docker, Kubernetes, Kafka, OpenSearch, Redis, etc.) — or the drive and curiosity to learn them quickly.

● Excellent debugging and problem-solving skills — comfortable owning features end-to-end.

● Strong system design and architecture judgment; knows tradeoffs and can explain them clearly.

● Strong product sense — understands tradeoffs, iteration speed, and value delivery.

● Comfortable in dynamic startup environments: adaptable, pragmatic and flexible.

● Deep “vibe coding” experience — has built and debugged systems end-to-end using different AI tools and methods, and is forward-looking enough to recognise that AI-assisted engineering is the future