The purpose of this role is to build and deploy next-generation capabilities that span a variety of novel use cases that rely on cutting-edge technologies and CV models. As well, review pattern recognition, robotics, computer science, data science, and computer vision. The role is responsible for creating new Machine Learning models and deploying them through the product life cycle to achieve a no code environment for the end users. The Machine Learning Engineer will provide a voice to the team by translating product goals to feature level architectures. They need to thrive in an entrepreneurial environment, evaluate existing and emerging imaging systems against use case requirements. As well as bring energy and clarity to the role. Success means service excellence and continuous improvement on product critical traits.

QUALIFICATIONS

• Bachelor’s degree or Master’s Degree in Computer Science or related field
• 3-5 years of ML engineering experience
• 5+ years of experience in product development

REQUIREMENTS

• Proficient in Computer Science and software engineering
• Experience with Pattern Recognition, Computer Vision, including Facial Recognition, and Data Science
• Experience working with machine vision APIs such as OpenCV or TensorFlow
• Strong Background in basic ML techniques including supervised, unsupervised, and deep learning
• Experience in major ML frameworks such as: PyTorch, TensorFlow, SparkML, or similarly used frameworks
• Strong analytical and critical thinking in problem solving
• Strong practical experience in Python, or C++
• Excellent communication and attention to detail
• Strong organizational and project management skills
• Solid mathematical foundation in understanding statistical evaluations and applied math.
• Experience in designing and building APIs and API management.
• Experience using version control systems
• AWS Professional or Specialty Certifications (or willingness to earn these during employment)
• Experience using AWS SageMaker to train and/or deploy a Machine Learning Model
• Experience deploying and updating a Machine Learning Model in a production environment

JOB PURPOSE

The purpose of this role is to build and deploy next-generation capabilities that span a variety of novel use cases that rely on cutting-edge technologies and CV models. As well, review pattern recognition, robotics, computer science, data science, and computer vision. The role is responsible for creating new Machine Learning models and deploying them through the product life cycle to achieve a no code environment for the end users.
The Machine Learning Engineer will provide a voice to the team by translating product goals to feature level architectures. They need to thrive in an entrepreneurial environment, evaluate existing and emerging imaging systems against use case requirements. As well as bring energy and clarity to the role. Success means service excellence and continuous improvement on product critical traits.

RESPONSIBILITIES

• Developing, Containerizing, and Deploying self-learning ML models
• Optimizing ML models to continuously improve accuracy and capabilities
• Connecting with databases and APIs for data transfer
• Ensuring cross-platform optimization for mobile devices
• Ensuring responsiveness of applications
• Working alongside graphic designers for web design features
• Seeing through a project from conception to finished product
• Ability to find and debug problems as they arise in their own code and others
• Ability to call, use, and create API’s
• Peer review other’s code
• Build, test, and deploy machine learning and computer vision use cases (confidential)
• Design and build predictive models to identify risks and opportunities to better manage inmate populations. Implement programmatic solutions to help predict recurring scenarios that are based on historical data
• Download a repository and get it running with limited assistance
• Staying abreast of developments in web applications and programming languages
• Identify new opportunities for data analysis and machine learning features that the data can accommodate
• Setup pipelines for training and testing ML models using AWS
• Build accessible and modular ML models and expose them as microservices using Flask/FastAPI/Docker
• Add unit and integration tests to the codebase
• Update task boards (GitHub and Jira) with daily progress notes in Confluence.
• Collaborate with other teams, including our Web, Mobile, and AI teams.