Job Summary

Expert, AI Application Sustenance – Engineer

We are seeking an experienced Support Expert to oversee the operational support, incident management, and technical maintenance of AI/ML-driven applications built using Python and deployed across AWS and Azure cloud platforms. The ideal candidate will have deep technical expertise in cloud infrastructure, Python-based application development, AI/ML operations (MLOps) and team leadership.

Key Responsibilities

1. Technical Leadership & Team Management

• Lead, mentor, and guide a team of support engineers (L1/L2/L3).
• Act as the primary escalation point for complex issues across applications, ML models, and cloud services.
• Drive continuous improvement initiatives and establish best practices for operational excellence.

2. Application Support & Troubleshooting

• Provide end-to-end support for Python-based APIs, microservices, AI/ML pipelines, and data processing systems.
• Diagnose and resolve issues related to Python environments, dependencies, performance bottlenecks, and integrations.
• Maintain high availability and optimal performance of production workloads.

3. Monitoring, Incident, and Problem Management

• Establish monitoring dashboards and alerts through Dynatrace, CloudWatch, Azure Monitor or Application Insights.
• Own the full lifecycle of incidents, from detection to root cause analysis and preventive measures.
• Maintain and ensure adherence to SLA commitments.

Skill Requirements

Technical Skills

• 5–10 years of experience supporting production systems, preferably AI/ML or data-intensive applications.
• Strong proficiency in Python, debugging, and API frameworks (FastAPI, Flask, Django).
• Experience with AWS (EC2, Lambda, S3, SageMaker, RDS) and Azure (App Services, Functions, Azure ML, Storage, AKS).
• Experience with CI/CD tools (Azure DevOps, GitHub Actions, Jenkins) and infrastructure-as-code.
• Familiarity with containerization (Docker, Kubernetes) and microservices architecture.
• Strong understanding of AI/ML concepts, model deployment and monitoring.

Other Requirements

• Excellent problem-solving skills and ability to handle high-pressure incidents.
• Strong leadership, mentoring, and team management capabilities.
• Effective communication and stakeholder management skills.

Preferred Qualifications

• Certifications such as AWS and Azure Administrator, or Azure/AWS Data/AI certifications.
• Experience with MLOps tools (MLflow, Kubeflow, Sagemaker Pipelines, Azure ML Pipelines).
• Knowledge of DevSecOps, automation, and cost optimization best practices.
• Experience in ITIL practices for incident, problem, and change management.