MLOps/DevOps Engineer II

Requirements

Overview:

The successful candidate will be instrumental in helping to build, test, and maintain the infrastructure and tools to allow for the speedy development and release of software and updates, as well as pushing the limits of a wide breadth of the tools and approaches to AI/Machine Learning for our advanced products and system using video information and IoT systems. The successful candidate possesses experience researching and developing new projects with a team, while applying knowledge communication ability to discussion on problem requirements and the planning of new ideas with the team and stakeholders.

Responsibilities:

Building and setting up new development tools and infrastructure

Designing, building, and maintaining efficient, reusable, and tested code in Python and other applicable languages and library tools

Understanding the needs of stakeholders and conveying this to developers

Working on ways to automate and improve development and release processes

Bringing your professional and high-level experience in working with video technologies in areas of preprocessing, neural nets, computer vision, etc.

Deploying Machine Learning (ML) to large production environments

Driving for continuous learning in the AI and computer vision fields

Testing and examining code written by others and analyzing results

Ensuring that systems are safe and secure against cybersecurity threats

Identifying technical problems and developing software updates and fixes

Working with software developers and software engineers to ensure that development follows established processes and works as intended

Planning out projects and being involved in project management decisions.

Qualifications:

Minimum of 3 years of hands-on experience with AWS services and products (Batch, SageMaker, StepFunctions, CloudFormation/CDK)

Strong Python experience

Minimum of 3 years of experience with Machine Learning/AI or Computer Vision development/engineering

Ability to provide technical leadership to developers for designing and securing solutions

Understanding of Linux utilities and Bash

Familiarity of containerization with Docker

Experience with data pipeline frameworks, such as MetaFlow (preferred)

Experience in Lambda, SQS, ALB/NLBs, SNS, and S3 (preferred)

Experience with AWS Fargate Serverless Computing Engine (preferred)

Practical experience deploying Computer Vision/Machine Learning solutions at scale into production

Exposure and/or understanding of technologies/tools such as: Keras, Pandas, TensorFlow, PyTorch, Caffe, NumPy, DVC/CML