Pythonic

Experience Requirements:

• 8+ years of relevant industry experience

Other Requirements:

• Strong Pythonic coding skills

• Experience in cloud computing platforms, especially AWS

• Familiarity with Boto3, WatchTower, CloudWatch, etc., and a practical understanding of cloud security

• Significant skillset in database technology and languages, such as SQL, DynamoDB, Redis, ElasticSearch, etc.

• Experience with scaling up ML models in cloud computing environments

• Significant experience with Docker and Linux systems

• Experience with other languages, ideally Java and JavaScript

• Knowledge of ML platforms, PyTorch, TensorFlow, etc.

• Driven by problem-solving

• Having the design mentality of less is the best

• A love for learning and intense curiosity

Responsibilities:

• Build backend systems and APIs to provide scalable solutions to our customers

• Work with data scientists, business analysts, front-end and data engineers, to define develop and improve products by automating or auto-assisting workflows for customers

• Lead the software development lifecycle, from conception to production

• Work with cloud security engineers to ensure the product meets security standards (e.g. SOC-2)

Other Requirements:

• Thorough understanding of Machine Learning fundamentals

• Experience building deep-net models

• Experience with PyTorch or TensorFlow

• Excellent problem-solving ability

• Understand cutting-edge algorithm developments and communicate with the rest of the team

• Thorough knowledge of Python

• Experience with NLP technologies and models

• Experience with OCR technologies

• An understanding of Adversarial models

• Experience with multi-threading bonus if it’s in Python

• Experience with cloud infrastructures, AWS, Azure, or G-cloud

• Relevant publications in highly recognized conferences/journals

• Good standing in various recognized competitions

• Having the design mentality of less is best

• A Love for learning and an intense curiosity

Responsibilities:

• Own the end-to-end solution to automate workflows involving intelligent document processing

• Research and develop the right ML/AI models to solve business problems

• Implement solutions and guide the deployment

• Guide the data engineering team on collecting training data

• Guide development of exception handling to provide an end-to-end solution