Degree in Artificial intelligence- AI
B2B, Bash scripting, Continuous Integration & Continuous Delivery - CI/CD, Django, Docker, Graphana, Java Programming, Python Programming, REST API, Shell Scripting language, SoapUI
We are looking for an SDE (AI Engineering) to join our growing AI team and build out the next generation of our platform. The ideal candidate is a hands-on backend application builder with significant experience in developing and deploying scalable systems. We’re looking for someone with experience in Python/Django application engineering and system design knowledge with fast implementation skills. They must have strong, firsthand technical expertise in configuration management and the proven ability to implement robust scalable solutions to manage large-scale AI model deployments and AI pipelines. They must be at ease working in an agile environment with little supervision. This person should embody a passion for continuous improvement and test-driven development/deployment.
Responsibilities
Things which will be exciting for you at work:
What you will not work on:
Qualifications:
What are we looking for?
We’re looking for someone with 1-2 years of experience in B2B, has a BS degree in computer science or similar, and is familiar with the following software/tools:
We have a great work environment with an exceptional bunch of engineers and programmers. We are an early-stage startup, and it implies putting your heart out to connect with customers and team members, solve hard & challenging problems, and build & maintain scalable systems. We are also quite conscious to build a highly collaborative & rich culture at Infilect. From 50 today, we expect to be 200 team member strong team in the next 2 years, serving global customers. If this interests you, come and join us on the rocket ship.
Infilect is a software company that specializes in providing artificial intelligence (AI) solutions for the retail industry. Infilect's core product is an AI-powered retail analytics platform called Swift, which helps retailers to gather and analyze data from various sources, such as social media, customer reviews, and in-store sensors. The platform then uses machine learning algorithms to provide insights and predictions about customer behavior,