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.

  2. Containerization & Deployment

  • 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.

  3. CI/CD Pipeline Implementation

  • Develop and maintain CI/CD pipelines for application and AI model deployment.

  • Automate build, test, and deployment processes using tools like Azure DevOps, GitHub Actions, or Jenkins.

  • Ensure smooth promotion of code and models across environments (dev, test, prod).

  • Troubleshoot pipeline failures and deployment issues.

  4. MLOps & AI Deployment Support

  • 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.

  5. Monitoring, Logging & Issue Resolution

  • 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 escalate when required.

  • Assist in root cause analysis and continuous improvement.

  6. Automation & Scripting

  • 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.

  7. Collaboration & Support

  • Work closely with Senior DevOps/MLOps Engineers, AI Engineers, and Development teams.

  • Support developers in environment setup, debugging, and deployment processes.

  • Follow DevOps and MLOps best practices and continuously improve operational workflows.

Desired Candidate Profile

• Bachelor’s degree in Computer Science, Engineering, or related field.

• 7+ years of experience in DevOps 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 DevOps, GitHub 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 DevOps / MLOps Engineer / Platform Lead

• Key Stakeholders:

  • AI Engineers & Data Scientists

  • Backend & Frontend Developers

  • DevOps / Platform Team

  • QA & Release Management Teams