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Mid-level role, metro location, and moderate brand create medium applicant competition.
Core cloud and DevOps skills are transferable, but ML platform specialization adds domain specificity.
Explicit 3–5 years plus mandatory platform, AWS and tooling requirements increases filter strictness.
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Own day-to-day operations, support, and maintenance of enterprise AI/ML platforms including Dataiku and Amazon SageMaker, ensuring availability, performance, and security compliance.
Design, develop, and deploy scalable AI/ML solutions and reusable platform components leveraging AWS AI/ML services (SageMaker, Bedrock, AgentCore, etc.) in collaboration with various technical and business teams.
Develop and maintain CI/CD pipelines, automate platform operations using infrastructure-as-code tools (CloudFormation, Terraform), and implement governance, monitoring, and cost optimization practices for AI/ML workloads.
3–5 years of experience in platform engineering, cloud engineering, DevOps, data engineering, or AI/ML platform support.
Hands-on experience with Dataiku or similar enterprise data science platform operations, and Amazon SageMaker including managing notebooks, models, endpoints, and pipelines.
Practical knowledge of AWS AI/ML services such as SageMaker, Bedrock, AgentCore, Amazon Q, QuickSight and relevant cloud services.
Experience with DevOps tools and practices including Git, Jenkins, Bash scripting, CloudFormation, Terraform, plus working knowledge of AWS IAM, networking, security, and monitoring.
Experienced in enterprise AI/ML platform operations combining cloud engineering, DevOps, and automation to support scalable AI/ML deployments.
Collaborates effectively with cross-functional teams such as data scientists, ML engineers, cloud teams, and business stakeholders to operationalize AI/ML solutions.
Skilled in infrastructure as code, CI/CD pipeline development, and governance implementation for AI/ML workloads within an enterprise context using AWS-native technologies.