ML Ops Engineer

Start: 2-3 weeks from date of offer Location: West Lafayette, IN - Primarily remote position but ability to come onsite every once in awhile would be needed. Length of Contract: 1 year + Hourly Pay: 100-105 per hour - W2 Contract Only Client is in need of a hybrid Machine Learning / Software Engineer. The position will not be purely focused round modeling - there will be some of this down the line - but have needs around CI/CD pipelining, edge development, Python, and Pytorch. Musts: Machine Learning experience. Heavy Python exp Pytorch edge development From the client: The role is for a Machine Learning Ops Engineer who can help transition our existing machine learning stack to a mature ML-TRL9 level. The current Pytorch-lightning and DVC based stack for visual segmentation models will be modernized to include full silos for dataset generation and ingestion as well as model tracking and evaluation. You will have autonomy to design these systems and deploy them to on premise compute for the term of the project as well as integrate these efforts into other ML efforts within the company. Additionally you will work with Data Scientist to design experiments, models, and techniques to train and deploy neural networks to embedded hardware. Primary Duties & Responsibilities: -Design, deploy, and maintain ML pipelines for both model tracking and dataset version control. -Design and implement data ingestion pipelines from novel 3D datatypes to annotation suites to accepted dataset version control -Work with data-scientist to train, evaluate, and deploy final models to TensorRT based deployments Job Qualifications: Bachelors in Computer Science, Statistics, Engineering or related field with 5+ years experience or Masters Degree. Solid Foundational understanding of CNNs, Transformers and other vision neural network architectures. Strong foundation in statistics and mathematics encountered in machine learning. Experience with Data Pipelines, modern databases, and workflow automation. Proficiency with Python and Pytorch, Skills in C++/C# a plus. Experience with Docker and other containerization/deployment solutions. Experience with DVC or other MLOps frameworks like MLflow. Experience with version control (Git) and collaborative development workflows.