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We’re looking for a Senior Backend Engineer to help design, build, and scale the systems that power FarmSee’s AI-driven products. You’ll work closely with the AI and QA teams to build robust, cloud-based backend services and data pipelines — balancing high performance with reliability and scalability. This is a hands-on role in a fast-moving startup, where impact, curiosity, and technical depth matter more than job titles.
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 recognize that AI-assisted engineering is the future
Key Skills
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