MLOps Engineer

1 Infrastructure Support Environment Management

Set up and maintain compute infrastructure including GPU-enabled environments. Configure and manage Linux-based systems for development and production environments. Provisioning and configuration of cloud and on-prem infrastructure. Monitor system resources and assist in performance tuning and optimization.

Build and manage containerized applications using Docker
. Deploy and manage applications on Kubernetes clusters under guidance from senior engineers. Creating deployment configurations Helm charts and environment setups. Support scaling and orchestration of microservices and AI workloads.

Develop and maintain CI CD pipelines for application and AI model deployment. Automate build test and deployment processes using tools like Azure Dev Ops
Git Hub Actions or Jenkins
. Ensure smooth promotion of code and models across environments dev test prod. Troubleshoot pipeline failures and deployment issues.

Deploying machine learning models and LLM-based services
. Integration of AI components into production systems. Contribute to model versioning
monitoring and lifecycle management. Work with AI engineers to operationalize RAG pipelines and inference services.

Implement and maintain monitoring and logging solutions e.g Prometheus
Grafana
ELK
. Track application performance system health and availability. Respond to incidents troubleshoot issues and escalat when required Assist in root cause analysis and continuous improvement.

Write scripts Python
Bash to automate repetitive operational tasks. Support Infrastructure as Code IaC initiatives using tools like Terraform or ARM templates
. Improve operational efficiency through automation and tooling.

Work closely with Senior Dev Ops MLOps Engineers
AI Engineers and Development teams. Support developers in environment setup debugging and deployment processes. Follow Dev Ops and MLOps best practices and continuously improve operational workflows.

• Bachelor’s degree in Computer Science, Engineering, or related field.
• 7+ years of experience in Dev Ops or platform engineering roles.
• Basic to intermediate experience with Linux system administration.
• Hands-on experience with Docker and containerization.
• Familiarity with Kubernetes (deployment and basic management).
• Experience with CI/CD tools (Azure Dev Ops, Git Hub Actions, Jenkins, etc.).
• Basic understanding of cloud platforms (Azure, AWS, or GCP).
• Scripting skills in Python, Bash, or similar.
• Understanding of version control systems (Git).
• Preferred Skills
• Exposure to AI/ML model deployment and MLOps practices.
• Familiarity with LLM deployment concepts and tools.
• Basic knowledge of GPU environments and high-performance computing.
• Experience with monitoring and logging tools (Prometheus, Grafana, ELK).
• Knowledge of Infrastructure as Code (Terraform, ARM templates).
• Understanding of microservices architecture.
• Key Performance Indicators (KPIs)
• Deployment success rate and pipeline stability.
• System uptime and availability.
• Resolution time for incidents and issues.
• Efficiency of CI/CD processes.
• Infrastructure utilization and basic cost optimization.
• Support effectiveness for development and AI teams.
• Stakeholders & Reporting
• Reports to:
  
  Senior Dev Ops / MLOps Engineer / Platform Lead
• Key Stakeholders: AI Engineers & Data Scientists
• Backend & Frontend Developers
• Dev Ops / Platform Team
• QA & Release Management Teams