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Posted about 1 month ago
Responsibilities & more
Responsibilities
1. Design and implement AI-driven applications using Python.
2. Build and maintain efficient, scalable, and automated data pipelines to support machine learning workflows.
3. Develop and deploy APIs and microservices that integrate ML models into platform ecosystems.
4. Manage the full ML lifecycle (training, validation, monitoring, and tuning) using MLOps best practices.
5. Apply DevOps practices (CI/CD, monitoring, automated testing) using tools like AWS CDK, CodePipeline.
6. Deploy solutions in the cloud using AWS services such as Lambda, S3, Glue, EventBridge, Step Functions and Sagemaker.
7. Monitor and troubleshoot production systems as needed.
Technical requirements:
1. 3+ years of experience in backend development or data engineering.
2. 3+ years of hands-on experience with AWS services.
3. 3+ years of experience in Python
4. Proven experience operationalizing ML models in production environments.
5. Proficiency with infrastructure-as-code tools (AWS CDK, CloudFormation).
Preferred qualifications:
1. Experience with AWS SageMaker.
2. Background in microservices architecture design and implementation.
3. Experience with CI/CD tools (Git, CodeBuild, CodePipeline).
4. Experience with Docker.
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