
Data Science Platform Engineer
2 days ago
A hands-on Machine Learning (ML) engineer plays a critical role in the development, automation, and deployment of ML pipelines across cloud environments.
Key Responsibilities:
- Deploy and maintain ML models using cloud services such as Sage Maker, Fargate, and Bedrock.
- Apply prompt engineering techniques to optimize Gen AI model performance and reliability.
- Assist in building and maintaining internal model-serving platforms to support development teams.
- Implement containerized services using Docker and deploy them to cloud infrastructure.
- Write Infrastructure-as-Code (IaC) using Terraform to automate cloud resource provisioning.
- Participate in unit and end-to-end testing of ML pipelines, services, and monitoring workflows.
- Support model monitoring and health tracking using Cloud Watch and internal observability tools.
Requirements:
- 3-5 years of experience in machine learning with strong programming skills in Python.
- Familiarity with cloud services such as Lambda, Glue, Cloud Watch, and Bedrock.
- Experience with container workflows (Docker) and model lifecycle management.
- Foundational knowledge of observability practices and model deployment fundamentals.
- Strong communication and documentation skills with a collaborative team mindset.
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