GCP AI/ML Engineer
Chicago, IL; Chicago, IL; Chicago, IL
R
A USD 123K-200K (estimate) Mid-level Full Time
Tasks
- Build enterprise AI platforms with automation observability and reliability
- Build production machine learning pipelines
- Deploy and manage Vertex AI pipelines
- Deploy online and batch predictions using Vertex AI endpoints
- Design automated workflows for ingestion feature engineering training evaluation and inference
- Design feature pipelines for training and inference datasets
- Develop standardized frameworks for governed ML deployments
- Drive documentation and standardization across ML workflows
- Enable automated model retraining and versioning
- Ensure model governance auditability lineage and compliance
- Establish best practices for MLOps pipeline orchestration and infrastructure management
- Implement CI CD pipelines for ML artifacts
- Implement model monitoring for accuracy data drift and concept drift
- Integrate BigQuery Cloud Storage and streaming sources
- Mentor and provide technical leadership for ML engineering practices
- Operationalize end to end ML lifecycle model training deployment monitoring and retraining
- Optimize model performance for scalability latency and cost
- Orchestrate ML workflows with Python Vertex AI BigQuery and Cloud Storage
- Secure handling of data and models using IAM
- Set up alerting and dashboards for proactive issue detection
Perks/Benefits
- N/A
Skills/Tech-stack
AI Pipelines | Apache Beam | BigQuery | CI/CD | Cloud Storage | Cloud platform | Concept drift | Containers | Data Drift | Docker | Feature Engineering | Feature Store | Google Cloud | Google Cloud Platform | IAM | Kubeflow Pipelines | MLOps | Machine Learning | Model Monitoring | PyTorch | Python | Scikit-learn | TensorFlow | Vertex AI | Vertex AI pipelines
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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