Job Description
We are seeking a skilled and experienced Machine Learning (Client) Engineer to join our team in a customer-facing role. You will #architect and implement innovative Client solutions, working closely with data scientists and engineers to put algorithms and models into practice to solve our customers' most challenging problems. You will take the lead in planning, designing, and running experiments, while researching new algorithms to deliver impactful solutions.
Key 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.
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.We are seeking a skilled and experienced Machine Learning (Client) Engineer to join our team in a customer-facing role. You will #architect and implement innovative Client solutions, working closely with data scientists and engineers to put algorithms and models into practice to solve our customers' most challenging problems. You will take the lead in planning, designing, and running experiments, while researching new algorithms to deliver impactful solutions.
Key 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.
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.