Summary: This Machine Learning Engineer is responsible for developing and deploying AI solutions within core business functions using cloud-native technologies. This role involves ensuring scalability, availability, and efficiency of AI deployments, and establishing best practices in AI operations. Candidates should have cloud-native experience with machine learning engineering and AI platforms in a large-scale environment. Experience operationalizing GenAI applications is not required, but will be an advantage.
Responsibilities:
Design and implement end-to-end machine learning pipelines that are fully integrated within cloud-native architectures, ensuring scalability and robustness.
Work closely with data scientists to operationalize machine learning models on cloud AI platforms, transitioning from experimental prototypes to production-grade solutions.
Optimize data architectures to enhance the performance and scalability of ML systems on cloud platforms: Azure
Lead the integration of ML models into existing and new system architectures, focusing on compatibility and high performance in a cloud environment. This includes designing and implementing robust APIs.
Collaborate with cloud architecture advisors to leverage advanced features of cloud technologies and AI platforms
Establish and evangelize best practices around AI Operations (including MLOps and LLMOps).
Continuously monitor, evaluate, and enhance the performance and efficiency of ML systems deployed on cloud infrastructures.
Skills:
Demonstrated experience with AI platforms on the cloud, such as Azure Machine Learning, Google AI Platform, or AWS SageMaker.
Strong proficiency in using major cloud services (Azure, AWS, GCP) for deploying ML models and managing data pipelines.
Proficient in Python, SQL, and cloud-native technologies such as Kubernetes and Docker.
Experience using Linux OS
Strong problem-solving skills, organizational abilities, and effective communication skills.
Experience operationalizing GenAI applications or assistants.