Staff ML Learning Engineer

AWS SageMaker Operations & Orchestration

•    Design and implement SageMaker Pipelines with complex DAG authoring for end-to-end ML workflows including data preprocessing, training, evaluation, and deployment

•    Configure and optimize SageMaker Processing Jobs, Training Jobs (PyTorch/HuggingFace containers), and Managed Spot Instances for cost-effective model training

•    Manage SageMaker Model Registry with versioning, lineage tracking, and approval workflows for model governance

•    Implement sophisticated endpoint deployment strategies including blue/green traffic routing, canary deployments, and A/B testing configurations

•    Configure and maintain SageMaker Model Monitor for continuous data quality monitoring, model quality assessment, bias detection, and feature drift alerting

GenAI Model Lifecycle Management

•    Build operational frameworks for Amazon Bedrock model deployments including provisioned throughput management, guardrail configuration, and usage monitoring

•    Implement automated evaluation pipelines for foundation model outputs with quality gates and human-in-the-loop review workflows

•    Design prompt versioning systems integrated with model registry for reproducible GenAI deployments

•    Monitor GenAI model performance including latency, cost per inference, hallucination rates, and guardrail trigger metrics

Model Lifecycle Automation

•    Develop automated retraining trigger systems based on data drift, performance degradation, scheduled intervals, or manual triggers

•    Implement evaluation gates as code with configurable thresholds for metrics (F1, precision, recall, AUC, etc.) before model promotion

•    Build automated model promotion workflows from dev → staging → prod with approval gates and rollback capabilities

•    Design rollback automation with traffic shifting strategies to quickly revert to previous model versions upon performance degradation

Training Data Engineering

•    Build AWS Glue ETL pipelines for training corpus assembly, transformation, and feature engineering at scale

•    Design dataset versioning systems using S3 + manifest files ensuring reproducibility and lineage tracking across model training runs

•    Establish reproducibility patterns including seed management, environment pinning, and deterministic data splitting

Container Engineering & Registry Management

•    Build optimized Docker containers for training and inference workloads with multi-stage builds, layer caching, and security scanning

•    Manage Amazon ECR repositories with lifecycle policies, image scanning, and vulnerability remediation workflows

•    Implement bring-your-own-container (BYOC) patterns for SageMaker supporting custom frameworks and dependencies

•    Optimize container startup times and resource utilization for cost-effective inference

ML Observability & Monitoring

•    Design comprehensive CloudWatch dashboards for model health including accuracy, latency, throughput, error rates, and drift metrics

•    Implement custom CloudWatch metrics for business-specific KPIs (e.g., clinical accuracy per condition, false positive rates for critical alerts)

•    Build alerting systems for accuracy regression, data drift, concept drift, and model staleness with appropriate escalation paths

•    Create observability frameworks that integrate with AWS X-Ray for end-to-end request tracing from API call through model inference

CI/CD for ML Artifacts

•    Design and implement GitHub Actions or AWS CodePipeline workflows for automated ML artifact testing, validation, and deployment

•    Build multi-stage promotion pipelines (dev → staging → prod) with automated testing gates and manual approval checkpoints

•    Implement artifact versioning and lineage tracking for models, datasets, feature transformations, and deployment configurations

•    Create integration testing frameworks for ML APIs including performance benchmarking and regression testing

Infrastructure & Compliance

•    Collaborate with Full Stack AI/ML Engineers to de