Posted:None|
Platform:
Hybrid
Full Time
Hi,
It was great talking to you. As discussed, here is the role and requirements for the position.
• Design, implement, and maintain data pipelines for processing large datasets, ensuring
data availability, quality, and efficiency for machine learning model training and
inference.
• Collaborate with data scientists to streamline the deployment of machine learning
models, ensuring scalability, performance, and reliability in production environments.
• Develop and optimize ETL (Extract, Transform, Load) processes, ensuring data flow
from various sources into structured data storage systems.
• Ensure effective model monitoring, versioning, and logging to track performance and
metrics in a production setting.
• Collaborate with cross-functional teams to improve data architectures and facilitate the
continuous integration and deployment of ML models.
• Work on data storage solutions, including databases, data lakes, and cloud-based storage
systems (e.g., AWS, GCP, Azure).
• Ensure data security, integrity, and compliance with data governance policies.
• Perform troubleshooting and root cause analysis on production-level machine learning
systems.
• Skills: Glue, Pyspark, AWS Services(ECS, ECR, NLB, ALB, ACM, IAM, Lambda, RDS, KMS,
API Gateway, Cognito, CloudFormation,Clouswatch), Strong in SQL; Nice to have :
Redshift, Knowledge of SAS Dataset
Reyika
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