ML Cloud Engineer

Responsibilities:
We are seeking an AWS ML Cloud Engineer to design, deploy, and optimize cloud-native machine-learning systems that power our next-generation predictive-automation platform. You will blend deep ML expertise with hands-on AWS engineering, turningdata into low-latency, high-impact insights. The ideal candidate commands statistics, coding, and DevOps—and thrives on shipping secure, cost-efficient solutions at scale.
Objectives of this role:
·  Design and productionize cloud ML pipelines (SageMaker, Step Functions, EKS) that advance predictive-automation roadmap
·  Integrate foundation models via Bedrock and Anthropic LLM APIs to unlock generative-AI capabilities
·  Optimize and extend existing ML libraries / frameworks for multi-region, multi-tenant workloads
·  Partner cross-functionally with data scientists, data engineers, architects, and security teams to deliver end-to-end value
·  Detect and mitigate data-distribution drift to preserve model accuracy in real-world traffic
·  Stay current on AWS, MLOps, and generative-AI innovations; drive continuous improvement
Responsibilities:
·  Transform data-science prototypes into secure, highly available AWS services; choose and tune the appropriate algorithms, container images, and instance types
·  Run automated ML tests/experiments; document metrics, cost, and latency outcomes
·  Train, retrain, and monitor models with SageMaker Pipelines, Model Registry, and CloudWatch alarms
·  Build and maintain optimized data pipelines (Glue, Kinesis, Athena, Iceberg) feeding online/offline inference
·  Collaborate with product managers to refine ML objectives and success criteria; present results to executive stakeholders
·  Extend or contribute to internal ML libraries, SDKs, and infrastructure-as-code modules (CDK / Terraform)
Skills and qualifications