AI Support Engineer / AI Application Support

Role:

Location:

• Provide L2/L3 support for AI, ML, and automation platforms/applications used across business functions.
• Monitor AI model performance, data pipelines, and application health to ensure stability and accuracy.
• Investigate and resolve incidents related to AI applications, including data issues, model behavior, and system failures.
• Act as a bridge between Data Science, IT, and business teams for AI-related support and enhancements.
• Support model deployments, versioning, retraining coordination, and post-deployment validation.
• Perform root cause analysis (RCA) for recurring AI or data-related issues and implement preventive actions.
• Validate data inputs, outputs, and anomalies impacting AI model predictions or automation outcomes.
• Support integration of AI solutions with enterprise systems via APIs and automation workflows.
• Maintain operational documentation, runbooks, and support procedures for AI applications.
• Ensure compliance with security, data privacy, and governance standards for AI solutions.
• Support UAT, releases, and production rollouts for AI and analytics solutions.
• Track and report support KPIs, SLAs, and performance metrics related to AI operations.
• Participate in continuous improvement initiatives to enhance AI reliability, scalability, and business value.
  

Required Skills & Qualifications

• Experience supporting AI/ML-based applications, analytics platforms, or automation tools.
• Strong understanding of data pipelines, model lifecycle, and AI operations (MLOps fundamentals).
• Knowledge of Python, SQL, or scripting for troubleshooting and analysis (support-level).
• Familiarity with APIs, cloud platforms, and monitoring tools.
• Experience in incident, problem, and change management within IT operations.
• Strong analytical and problem-solving skills with attention to detail.
• Ability to work with cross-functional global teams.
• Good communication and stakeholder management skills.
  

Preferred / Nice-to-Have

• Exposure to Azure AI, AWS AI/ML, or Google Cloud AI platforms.
• Experience with MLOps tools (e.g., MLflow, Azure ML, Kubeflow).
• Understanding of data governance, ethics, and responsible AI.
• Experience in a Global Capability Center (GCC) or enterprise support environment.
• ITIL knowledge or certification.