AIVF is a well-funded startup company aiming to transform In-Vitro Fertilization (IVF).

We are a team of experts with decades of clinical, technology and business experience focused on bringing advanced artificial intelligence (AI) to optimize every phase of the fertility journey.

The company’s product (EMA™) is software platform that uses large, diverse clinical data sets and sophisticated algorithmic engines to assist automated and intelligent decision-making in IVF clinics.

We are looking for someone who will:

Be an experienced software engineer for our next-generation cloud-based platform and to lead the development of our related SaaS products.

As a Senior Cloud Engineer, you will work with customers, partners, and developers to enable our AIVF SaaS solutions. You will be a subject matter expert and will work to transfer knowledge (internally and externally), design the application stack, architect integrated solutions and execute them to drive customer satisfaction. You will also highlight best practices, perform architectural design work and development, test, and publish documentation.

Requirements:

• A bachelor’s degree in Computer Science or engineering-related field and/or an elite IDF technology unit graduate with relevant experience
• 6+ years of experience in Python/Go/Node/Java/C#/C++
• 3-4+ years’ experience in a cloud environment (AWS preferred) and have a deep understanding of SaaS concepts
• Extensive experience with a variety of technologies (such as Python, Docker, kubernetes)
• UNIX / LINUX tools and environment
• Self-starter, ability to work independently
• Strong verbal and written communications skills
• Strong presentation skills
• You have experience in enterprise-scale application development in a cloud/SaaS environment (preferably using AWS)
• You are proactive by nature, strive for constant improvement, and have a keen sense of ownership
• You have a solid understanding of software security and networking

Nice to have:

• Understanding of microservice-based architecture
• Understanding of data pipelines, ETLs, and common tools used in the process