Production Support Engineer

ASM Tech Solutions; United States; Hybrid; USD 90,000 - 150,000; Full time

Job summary

ASM Tech Solutions is seeking an experienced L2 Production Support Engineer to monitor and troubleshoot AI platforms, cloud infrastructure, and data pipelines in a hybrid US-based setup. The role emphasizes deployment, automation, and proactive incident management.

You will work with DevOps, MLOps, data engineering, and platform teams to improve CI/CD and operational reliability, with visibility into observability and incident trends.

Qualifications

• 3–5 years in Level 2 application, platform, DevOps or production support roles.
• Experience troubleshooting cloud-native apps, distributed systems, containers, and Kubernetes- based environments.
• Strong knowledge of CI/CD pipelines and deployment automation (GitLab or similar).
• Experience with monitoring, logging and observability tools.
• Familiar with ITIL incident, problem, change and release management processes.

Responsibilities

• Monitor, troubleshoot, and resolve Level 2 production incidents across AI platforms and cloud infrastructure.
• Provide hands-on support for deployment and orchestration of AI/ML workloads.
• Build and maintain monitoring, alerting, and observability for infra and data pipelines.
• Perform root cause analysis and drive permanent fixes for production issues.
• Collaborate with DevOps, MLOps, data engineering, and platform teams on CI/CD and automation.
• Develop automation and self-service tooling to reduce manual workload.
• Monitor batch processes, data ingestion, model training, and deployment failures.
• Apply ITIL-based incident and change management to production operations.
• Analyze support-ticket trends and implement operational improvements including AI-driven automation.

Skills

UNIX/Linux

SQL

Scripting (Python/Shell)

CI/CD

Monitoring/Observability

Tools

GitLab

Docker & Kubernetes

Splunk

Grafana / AppDynamics / Prometheus

Terraform / Ansible

Job description

As of now regular shift during US hours. Hybrid work ( 3 days from Office is Mandatory and 2 days remote)

Location: Lake Mary or 240 NY Office

Key Responsibilities

• Monitor, troubleshoot, and resolve Level 2 production incidents across AI platforms, cloud infrastructure, data pipelines, model-serving environments, and associated services.
• Provide hands-on support for deployment, orchestration, and operational management of AI/ML workloads across cloud-native environments.
• Build and maintain monitoring, alerting, and observability capabilities for infrastructure, applications, data pipelines, model operations, and distributed compute workloads.
• Perform root cause analysis for production issues, implement permanent fixes, and drive problem-management activities to improve service reliability.
• Collaborate with DevOps, MLOps, data engineering, platform engineering, and application teams to maintain and enhance CI/CD and deployment automation.
• Develop and support automation, self-service tooling, recovery mechanisms, and self-healing controls to reduce manual operational effort.
• Monitor and troubleshoot batch processes, workflow orchestration, data ingestion, model training, and model deployment failures.
• Apply ITIL-based incident, problem, change, and release management processes to support stable production operations.
• Analyse support-ticket and incident trends; recommend and implement operational improvements, including AI-driven automation where appropriate.

Qualifications & Skills Mandatory:

• 3-5 years of experience in Level 2 application, platform, DevOps, or production support roles.
• Strong hands-on experience with UNIX/Linux, SQL, and shell or Python scripting.
• Experience troubleshooting cloud-native applications, distributed systems, containers, and Kubernetes-based environments.
• Working knowledge of CI/CD pipelines, deployment automation, and source-control platforms such as GitLab.
• Experience with monitoring, logging, and observability tools such as Splunk, Grafana, AppDynamics, Prometheus, or similar tools.
• Understanding of incident management, root cause analysis, problem management, and ITIL support processes.
• Strong analytical and problem-solving skills, with a client-service mindset.
• Ability to troubleshoot data-pipeline, workflow, API, and production deployment issues.

Good-to-Have:

• Exposure to MLOps practices, including model deployment, model monitoring, feature/data pipelines, and AI workload orchestration.
• Experience with cloud platforms such as AWS, Azure, or GCP.
• Experience with infrastructure automation and configuration-management tools such as Ansible, Terraform, or Ansible Tower.
• Familiarity with workflow and job-scheduling tools, such as Contro-M or equivalent enterprise schedulers.
• Knowledge of Docker, Kubernetes, and distributed compute/data-processing technologies.
• Exposure to Kafka, MQ, or other messaging and event-streaming platforms.
• Experience implementing self-healing, auto-remediation, resilience, backup, or disaster-recovery mechanisms.
• Familiarity with AI-driven operational tooling, ticket-trend analysis, or automation agents.