Sr. Machine Learning Solutions Architect
Tasks
- Define ML deployment approaches and production infrastructure
- Deploy and monitor ML models for business use
- Design secure scalable data science environments and tooling
- Develop reusable assets playbooks and delivery frameworks
- Ensure reliability observability through documentation logging monitoring
- Execute operational testing QA validation and performance testing
- Extract data from source systems into model training environments
- Lead workshops discovery sessions architecture and design reviews
- Mentor and coach team members
- Own end to end ML architecture and delivery
- Perform code reviews documentation testing and governance
- Recommend technologies for inference retraining monitoring and lifecycle management
- Translate business and data science requirements into scalable solutions
Perks/Benefits
Skills/Tech-stack
API Development | AWS | Amazon EMR | Amazon Redshift | Apache Spark | Azure | Cloud platform | Databricks | Distributed Systems | Django | Flask | Google Cloud | Google Cloud Platform | HDFS | JMS | Java | Kafka | Linux | Logging | MLOps | Machine Learning | Monitoring | MySQL | Observability | Oracle | Python | SAP | SQL | Scala | Snowflake | Spring | Testing | Web development
Education
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