Contractken

Looking to hire a single backend engineer who will become a part of the core team, with the track to become a part of the leadership at our company.

Candidate should have worked in startups, possess strong programming skills in Python, displays a sense of ownership and is highly energetic and eager to learn state-of-the-art tech and ML.

A minimum of 2 years of relevant experience is preferred.

2022 or later year pass outs - please apply only if you have relevant full-time and at least 2 prior internships experience

Representative (not exhaustive) responsibilities:

• Assess the current design and infrastructure of the backend server, and continuously implement enhancements to make the systems more robust, reliable and responsive
• Design, implement, test, and deploy APIs with Python on Linux servers
• Enhance back-end functionality and integrate with the front end to deliver a fully functional product
• Implement and set up automated tests to periodically test backend infrastructure for reliability, security and performance
• Design new data pipelines to enable the production deployment of new models
• Mentor junior employees and interns in the company
• Additionally, interest and ability to contribute to the continuous enhancement of our state-of-the-art NLP model pipeline and Inferencing engine is a big plus
• Be ready to roll up your sleeves and participate in customer calls, understand our ICP well, do research around product features, etc.

Fast Track Your Application

Think about the ICP for ContractKen. Sign up for a trial plan, experience the product and provide feedback on how to improve the current experience of the product to add immediate value to the ICP. We don't need you to fully code any feature in its entirety and are not looking for free work. The idea is to identify folks who get** what we are trying to do with ContractKen and find faster alignment with the right candidate.

Do not apply if

You're looking for your first, real-world model building, ML/AI role. We believe that deploying existing models (open source and proprietary) in production and delivering real business value to our customers is 10x more important than hyperparameter tuning or the latest algorithm implementation. While we'd love to entertain experienced ML data scientists, the emphasis on MLOps is as much as it is on experimentation.