Machine Learning Engineer

Glow Networks - Dallas, TX

Full-time

Hybrid - Dallas, TX

Professional, Scientific, and Technical Services

About the position

The Machine Learning Engineer will play a crucial role in architecting and implementing innovative client solutions in a customer-facing capacity. This position involves collaborating with data scientists and engineers to apply algorithms and models to solve complex problems, while also leading experiments and researching new algorithms to deliver impactful solutions.

Responsibilities

• Design, build, and deploy machine learning models within the proposed platform, ensuring they are optimized for performance and scalability.
• Collaborate with Data Scientists and Data Engineers to implement feature stores, model management (MLOps), and Explainable AI (XAI) capabilities.
• Monitor and optimize the performance of deployed models, ensuring they meet business requirements and performance standards.
• Support model management, versioning, and deployment workflows to streamline the operationalization of machine learning models.
• Engage directly with customers to understand their business problems and help implement tailored Client solutions.
• Deliver Machine Learning projects end-to-end, including understanding business needs, planning projects, aggregating & exploring data, building & validating predictive models, and deploying completed Client capabilities on the AWS Cloud to deliver business impact.
• Utilize deep learning frameworks like PyTorch and TensorFlow to build computer vision models for versatile applications.
• Work on large-scale datasets, creating scalable, robust, and accurate computer vision systems.
• Collaborate with Cloud Architects to build secure, robust, and easy-to-deploy cloud-native machine learning solutions.
• Work closely with customer account teams and product engineering teams to optimize model implementations and deploy cutting-edge algorithms.
• Assist customers with Machine Learning Operations (MLOps) workflows such as model deployment, retraining, testing, and performance monitoring.
• Apply best practices from core Software Development activities to Machine Learning, including deplorability, unit testing, and structured, extensible software development.

Requirements

• Proven experience in building and deploying machine learning models at scale.
• Proficiency with deep learning frameworks like PyTorch and TensorFlow.
• Experience with cloud-native machine learning solutions, preferably on AWS.
• Experience with Databricks.
• Experience with Agile Methodology.
• Strong understanding of MLOps workflows, including model management.
• Ability to work independently and collaboratively with cross-functional teams.