AI/ML Engineer

Role: AI/ML Engineer

Experience: 5+ years Location: Remote

Shift Timing: Normal Day Shift

Workdays: Friday – Tuesday

Weekly Off: Wednesday & Thursday

Job Description

• Support and monitor weekend batch loads, ensuring timely and accurate execution.
• Perform outlier detection and root cause analysis for issues such as demand forecast drops or anomalies.
• Translate findings into clear insights and communicate them effectively to leads and stakeholders.
• Monitor scheduled jobs and data pipelines to ensure successful execution.
• Validate dashboard outputs and analyze data trends, anomalies, and inconsistencies.
• Support basic model validation and output analysis (forecast vs actuals, error trends).
• Write and optimize SQL queries and PySpark transformations for debugging and analysis.
• Identify patterns in recurring failures and recommend improvements.
• Work with data science and engineering teams to debug model- or data-related issues.
• Escalate issues when required, with clear analysis and supporting insights.
• Monitor workflows in Azure Data Factory (ADF) and Databricks.
• Contribute to CI/CD validation and release monitoring.
• Maintain documentation for issues, RCA findings, and fixes.

Required Skills

• Strong working knowledge of SQL (Must Have).
• Basic to Intermediate PySpark / Python.
• Understanding of data pipelines and distributed data processing.
• Experience with Azure Data Factory (ADF).
• Experience with Databricks.
• Basic understanding of Machine Learning workflows.
• Experience in anomaly detection and root cause analysis.
• Strong stakeholder communication skills.
• Experience in dashboard validation and data analysis.

Specific Contribution Expected from the Role Responsible for monitoring batch pipelines, validating dashboards, and performing anomaly detection with root cause analysis. Requires strong SQL, working knowledge of PySpark/Python, and experience with data pipelines, Azure Data Factory, Databricks, and basic ML workflows. Involves stakeholder communication, CI/CD monitoring, and continuous improvement through debugging, pattern identification, and data-driven insights.