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Required Skills: - Python - MLOps - Cloud Platform - Incident Management - Monitoring & Observability - Containers & Orchestration - CI/CD - Team Leadership Nice to Have: - DevSecOps - Cloud Certifications 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. This role ensures high availability, reliability, and performance of AI systems that support business-critical processes. 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. Required Qualifications Technical Skills 510 years of experience supporting production systems, preferably AI/ML or data-intensive applications. Solid 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. Soft Skills 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. .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.