MLOps Support Engineer

CloudFactory

. Provide Tier 1 and Tier 2 operational support for AI/ML solutions .

Posted 9/24/2026full-timeKathmandu • NepalMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in providing operational support for AI/ML solutions, including troubleshooting, monitoring, and incident management. Proficient in SQL, Python, and cloud platforms, with a strong focus on model performance and bias prevention.

Highest-signal resume keywords

Operational Support for AI/ML SolutionsSQL ProficiencyPython ScriptingMonitoring and Observability ToolsMLOps Tooling

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

SQLPythonBashMLOpsGitKubernetesAI/ML SystemsModel Performance MonitoringIncident ManagementData Pipeline Health Monitoring

Soft Skills

Troubleshooting SkillsCollaborative MindsetAttention to Detail

Tools & Technologies

AWSGCPAzureGrafanaPowerBINew RelicMLflowDatabricks

Industry Keywords

DevOpsSREPlatform SupportModel DriftBias Prevention

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformGrafanaKubernetesPythonSQL

About the role

Key responsibilities & impact

Provide Tier 1 and Tier 2 operational support for AI/ML solutions
Identify failed jobs, degraded pipelines, and performance anomalies
Triage incidents, investigate issues, and coordinate escalation to Tier 3 Engineering
Participate in on-call rotas once established
Validate successful completion of pipelines and jobs
Monitor data pipeline health, model execution, and basic performance metrics
Identify operational issues before they impact customers
Respond to or alert customers about model outages or issues
Support incident management, rollback, and recovery activities
Use and maintain runbooks and operational documentation
Work with Engineering to improve supportability and observability
Contribute to knowledge sharing to reduce single points of failure
Work within defined SLAs and support processes
Build quarterly business reviews on ML model health
Evaluate champion/challenger models for promotion decisions
Monitor model drift and performance degradation
Validate that model updates and added data do not introduce bias
Provide MLOps coverage through assigned shift rotas and rotational on-call work

Requirements

What you’ll need

Experience in operations, DevOps, SRE, or platform support roles
Strong troubleshooting skills in production environments
Proficiency in SQL and scripting, including Python and Bash, for developing and automating ML workflows
Familiarity with cloud-hosted systems, including AWS, GCP, or Azure, for cloud-based ML services
Solid understanding of Git and version control in collaborative development environments
Comfortable working from runbooks and structured processes
Exposure to AI/ML systems in production
Familiarity with monitoring and observability tools such as Grafana, PowerBI, or New Relic
Knowledge of MLOps tooling and data platforms such as MLflow and Databricks
Experience supporting customer-facing platforms
Knowledge of containerization, including Kubernetes, is a plus
Experience with LLM prompt engineering and troubleshooting
Early career in MLOps or ML Engineering
Background in computer science, informatics, or related fields
Passion for machine learning and AI, with enthusiasm for optimizing and maintaining ML models in production environments
Collaborative mindset and willingness to contribute to model improvement, A/B testing, and iterative development
Attention to detail regarding model performance, bias prevention, and model behavior

Benefits

Comp & perks

Platform for professional growth, impact, and community
Opportunities to earn with purpose, learn every day, and serve a mission
Global community and collaboration across diverse cultures and perspectives