We are looking for a Model Integration Trainee to support integration workflows, data pipelines, and automation processes across enterprise systems. This role is ideal for someone starting their career in AI workflow integration, scheduling systems, and data operations .
You will learn how to support model workflows, monitor jobs, troubleshoot basic failures, work with scheduling tools, and assist in integrating AI models with enterprise data systems.
Key Responsibilities AI & Model Workflow SupportAssist in running and monitoring AI/ML pipelines.
Help validate model output, logs, and workflow results.
Support senior engineers in integrating models with scheduling tools and data systems.
Learn how job scheduling tools (like Control-M or Airflow equivalents) trigger model workflows.
Assist in monitoring job runs, identifying failures, and escalating issues.
Support simple tasks such as retrying jobs, checking logs, and validating job status.
Assist in handling basic ETL components inspired by DataStage-like tools (data extraction, transformation checks).
Support debugging simple pipeline failures under guidance.
Help organize datasets and support data preparation for model workflows.
Learn how model workflows run on Unix/Linux environments.
Support writing or modifying simple shell scripts (under supervision).
Assist with basic Python utilities for data parsing or workflow automation.
Document workflow steps, test results, and integration maps.
Support senior engineers during migrations, environment updates, or agent installations.
Participate in team discussions and help track tasks.
Interest in AI model integration, scheduling tools, and data workflows .
Basic understanding of Python and Linux commands.
Eagerness to learn ETL/ELT concepts and workflow automation.
Strong analytical mindset and willingness to troubleshoot.
Good communication skills and ability to work with senior engineers.
Familiarity with job schedulers (e.g., Control-M, Airflow, or Cron jobs).
Basic understanding of SQL or relational databases.
Exposure to cloud data platforms (Snowflake is a plus).
Any experience with scripting (Shell/Python) or integration concepts.
How enterprise AI/ML workflows are triggered, monitored, and integrated.
Basics of job scheduling, automation rules, and calendar policies.
How to support ETL/data pipeline debugging and data flow tracking.
Fundamentals of API-based integrations and enterprise data orchestration.
Hands-on exposure to Unix, shell scripting, and automation best practices.
Understanding Snowflake, SQL logic, and data warehouse integration patterns.
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